This PDF file contains the front matter associated with SPIE Proceedings Volume 12788, including the Title Page, Copyright information, Table of Contents, and Committee Page

In order to adapt to the demand analysis of power grid frequency modulation capacity under the rapid development of new energy and serve the construction of power frequency modulation market, a new power frequency modulation capacity demand assessment method considering the fluctuation of new energy is proposed. The purpose of reserving power frequency modulation capacity is to smooth the power generation deviation during the real-time operation of the power grid and keep the power grid frequency stable. At present, the deviation of power generation and consumption is mainly caused by the fluctuation of power load and the fluctuation of new energy output. Based on the analysis of the volatility of new energy and load, this paper proposes a method of power frequency modulation capacity demand assessment based on the volatility assessment. In this method, new energy and load fluctuation are regarded as independent variables, and the demand of power frequency modulation capacity is determined with the goal of maximizing the fluctuation of power generation and consumption. Finally, an example is given to verify the effectiveness of the proposed method based on the actual grid data of a province. The calculation results show that the evaluation results obtained by this method increase by 12.5% compared with the traditional method, which is more in line with the power grid operation requirements under the large-scale development of new energy.

Based on the fault tree analysis method of fuzzy set theory and expert evaluation, the failure evaluation of the micro-flow control valve which used in hydrogen supply system without failure statistics was carried out. The qualitative and quantitative analysis of the failure of the micro-flow control valve was completed, sealing performance is the key factor that affect the safe and stable operation of the valve. The failure probability of each basic event was obtained, the maximum failure probability was 9.718x10-3. Compared the sealing performance of four different commonly used packing materials, such as UPE, graphite, PEEK and PTFE, it was concluded that UPE still maintained good sealing performance and low friction torque under ultra-high pressure. It is an ideal packing material for micro-flow control valve of hydrogen supply system.

How to meet the interests of all parties while optimizing the allocation of resources and to distribute the benefits fairly and reasonably is an urgent problem for integrated energy systems in industrial parks. Based on this, the paper proposes a cost sharing and benefit distribution model of integrated energy system in industrial parks based on cooperative game. Firstly, the characteristics of the independent and cooperative operation modes of each participating entity in the integrated energy system of industrial parks are analysed, and on this basis, a model of the cooperative game of multiple participating entities in industrial parks is established, and a method for calculating the cooperative surplus is proposed. Finally, the validity of the method is verified through simulation. Finally, the validity of the method is verified through simulation. It can provide a reference for the research on the cost sharing and benefit allocation mechanism of the integrated energy system

In recent years, as global warming has become more severe, El Niño has swept the world, glaciers have melted, river flows have decreased, and freshwater scarcity has become increasingly frequent around the world. Moreover, the uneven distribution of freshwater resources and the lack of effective management of industrial and domestic wastewater emissions in some developing countries have strained the already scarce freshwater resources. In this study, a new water desalination system is invented to make the solar distilling process more efficient and intelligent. The system includes major components such as a water tank, outdoor solar water heaters, an advanced solar distiller, and hydraulic components such as vacuum pumps and check valves. At the same time, the project uses intelligent sensing devices such as flow meters, temperature sensors, and light intensity sensors, while the system can connect to Wi-Fi through the ESP8266 module to transmit sensor data back to Alibaba Cloud servers. What’s more, the project group uses neural network algorithms to optimize the parameters of the system, which makes the whole system learn by itself so that it can be more efficient.

To improve the energy use and reduce the carbon emissions of the integrated energy system in facility agro-industrial parks, this paper considers the time-shiftable characteristics of the load side of the park and the real-time power output adjustment of the energy supply equipment on the energy supply side for the optimal scheduling of the integrated energy system of the park, and proposes a strategy to realize the near-zero carbon operation of the integrated energy system of the industrial park of facility agriculture under the condition of economic constraints. The model aims at minimizing the operation cost and carbon emission of the integrated energy system in the park, and the model is solved by an improved sine cosine algorithm. Finally, the effectiveness of the proposed strategy is verified by selecting the integrated energy system of an agricultural industrial park in Northwest China.

Today's Internet technology is changing rapidly, the value of information in power systems is increasing. In the renewable energy-rich power grid, relatively accurate renewable energy prediction can reduce uncertainty caused by the random output of renewable energy, thus effectively improve the efficiency of renewable energy use, but also for power companies and power grid companies to provide a reference for economic benefits. In this paper, the value of photovoltaic (PV) predictive information to power systems is evaluated using an innovative evaluation system including Shannon entropy and non-noise ratio. Finally, taking IEEE 30-bus system as an example, the design and implementation of this paper are verified.

With the global energy shortage and the increase of power demand, the peak-valley difference in the power market is constantly expanding, which increases the cost of traditional industrial parks. This paper takes the integrated energy system (IES) of industrial parks as the research object, considers the price transmission mechanism between IES and operators. The goal is to reduce the cost of integrated energy systems. In this paper, particle swarm optimization is adopted to solve the integrated energy system model. It is proved that the operation cost can be reduced effectively.

Renewable energy will have unprecedented development opportunities with the implementation of Emission peak and Carbon neutrality strategy, while promoting the consumption of renewable energy also face huge challenges. Thus, microgrid is known as an important solution of distributed renewable energy consume. This paper firstly designs a multienergy complementary microgrid system composed of wind power, photovoltaic, diesel generators, energy storage batteries, a wind-solar-diesel-storage microgrid simulation model has been established. Secondly, an operation control strategy suitable for the independent microgrid is proposed for priority utilization of renewable energy. According to variations of wind turbine and photovoltaic output power, the operation mode of the microgrid is adjusted through the energy storage system in order to fully consumption of the renewable energy, while the user demand could be satisfied. Finally, a multi-energy complementary microgrid system simulation model is built based on the TRNSYS platform, which verify the effectiveness and feasibility of the control strategy, provides theoretical and methodological support for the consumption of distributed renewable energy in the future.

Compressed air energy storage technology is one of the most promising energy storage technologies. Setting throttle valve in the system is conducive to ensuring the stable output of the system. A throttle valve is set in an adiabatic compressed air energy storage system with four stages of compression and four stages of expansion. The effects of the setting of the initial pressure ratio of the throttle valve on the inlet conditions of the expansion section, the output capacity of the expander, the total output capacity of the system and the system efficiency are studied. It is found that the increase of the initial pressure ratio is conducive to improve the system efficiency, prolong the energy release time of the system, reduce the output power of the system per unit time, and there is a throttle pressure ratio to maximize the total output power of the system.

Aiming at solving the problem integrating the low-grade heat recovery from waste water and the high-temperature hot water demand for the printing and dyeing industry, the single-stage heat pump model and cascade heat pump system model are built and analysed. Two refrigerants, including R134a and R152a, are selected separately as the working fluids of single-stage heat pump. Meanwhile, these refrigerants are used in the cascade heat pump for two joint systems. For different heat pump systems, the performance parameters are calculated and compared through the simulation of thermodynamic process, with the help of EES and MATLAB. The results show that COP of cascade heat pump is generally greater than that of single-stage heat pump system, for various evaporation temperature and condensation temperature. For the heating power and exergy, the performance of the cascade heat pump is between the single-stage heat pumps with corresponding different refrigerants chosen. Considering the compression process of heat pump system, the compression ratio in the single-stage heat pump cycle is far greater than that of the corresponding cascade heat pump. In order to ensure the practicability and safety of the system, the performance of the cascade heat pump is better.

Under the goal of "double carbon", deep peak regulation of coal-fired units is one of the important means to absorb new energy generation and improve the stability of power grid. In this paper, EBSILON software is used to build the heat storage and peak regulation model of secondary reheat unit, with 30% THA as the base condition to discuss the influence of main steam and primary reheat cooling section steam on the peaking capacity, the feasibility of heat storage and peak regulation and the utilization rate of the system. The results show that the main steam is more efficient than the primary reheat cooling steam in the process of reducing the unit load, improving the unit thermal efficiency in the process of heat release.

Currently, the main operating heat and cooling storage technologies are all single-function energy storage technologies, which are inhibited from large-scale application in urban buildings due to factors such as space occupation in distributed buildings. Therefore, based on the background of "double carbon" target, this paper constructs a compact latent heating and cooling system based on the study of devices and materials for phase change heating and cooling storage. In this paper, the volume configuration of PCM is optimized by selecting sugar alcohol material with low phase change temperature and high phase change enthalpy as PCM and combining heating and cooling storage technologies to reduce the volume of thermal storage materials by 39.72% without affecting the heating capacity. Under the policy of peak-valley electricity tariff, this paper uses the Life Cycle Cost (LCC) method to prove the economy of the system. The use of the heating and cooling storage system proposed in this paper can reduce electricity expenses by about 34.36% in the heating season and 49.51% in the cooling season. By comparing the LCC of the system, the LCC of CLHCS is only 77.5% of the system without energy storage.

The second and third class oil reservoirs in Sazhong Development Zone of Daqing Oilfield are distributed horizontally and longitudinally, and it is difficult to subdivide and develop. The third class reserves of the second class oil reservoirs account for 41% (25.9% off-balance sheet). In order to realize simultaneous exploitation of reservoir potential in Pu II group, the three types of reservoirs that are difficult to be used by chemical flooding should be explored in the water flooding stage. However, the three types of reservoirs have the characteristics of poor development and poor connectivity, so it is necessary to achieve fine exploitation through measures. Taking BQ block of Sazhong Development Zone of Daqing Oilfield as an example, this paper has achieved good results by optimizing potential Wells, designing reasonable fracture penetration ratio and optimizing fracturing technology. Field practice has proved that compared with ordinary perforating production, the strength of sandstone production in fractured well is increased from 2.48m³ /d.m to 5.1m³ /d.m, and daily oil production is increased by 3.76t.

Class II and III oil reservoirs in the Sazhong Development Zone of Daqing Oilfield are horizontally and vertically alternately distributed, making it difficult to subdivide development. In order to simultaneously tap the potential of Class II and III oil reservoirs and improve the production degree of poorly Class III oil reservoirs, this paper takes MN Block in Sazhong Development Zone of Daqing Oilfield as the research object, and forms an economic and effective perforating method for simultaneous development of Class II and III oil reservoirs through optimizing the perforating principles, perforating processes and perforating fluids, providing reference significance for the simultaneous development of Class II and III oil reservoirs in Sazhong Development Zone.

In the period of global energy low-carbon transition, hydrogen energy is regarded as a new solution to reduce carbon emissions as a secondary energy source with the advantages of high thermal energy, zero pollution, easy storage and easy transportation, which can effectively promote the development of the existing energy system to a new and optimized direction. In this paper, an integrated energy microgrid system including a proton exchange membrane electrolyzer, a fuel cell and a hydrogen storage tank is established, and the start-stop power of the proton exchange membrane electrolyzer and the fuel cell is optimally scheduled by applying a fuzzy control strategy, and the feasibility of the proposed integrated energy microgrid system is verified by using MATLAB software simulation

In order to explore the reservoir potentian of outer expansion, in-depth geological study and reserve potential prediction analysis of the reservoir in the outer expansion area of transitional zone of X block were carried out. Firstly, based on the study of oil-water co-formation in the block, the oil bearing boundary is re-determined by using the well seismic combined with structural research technology and the interpretation results of oil bottom and water top. Secondly, sand body thickness is predicted by comprehensive comparative analysis of sedimentary facies zone map, sand body thickness contour map, seismic reservoir prediction map, amplitude slice and root mean square amplitude slice. Thirdly, numerical simulation technology is used to predict capacity potential. The oil-water boundary line of external expansion is predicted, and the reservoir potential is estimated by subdivision of calculation units. This study confirms that some depositional units in the transitional zone have reservoir development, and have the potential of expanding edge and expanding reservoir, which can provide practical experience for expanding and tapping potential outside the transitional zone.

Single crystal cathode materials have better cycling stability than the polycrystalline ones, since they own smaller specific surface area and better mechanical strength, thus suffering from reduced contact with electrolyte and suppressed generation of cracks during long-term cycling. Consequently, in this work, the single crystal Li-rich materials were synthesized using a molten salt method towards solving the problem of fast capacity decline. We investigated the role of calcination temperature on the structure and performance of synthesized Li-rich materials, and found out that higher temperature is beneficial to the particle growth and structural stability, but it also leads to lattice shrinkage and increased Li2MnO3 phase, which deteriorated the electrochemical performance. Therefore, the calcination temperature is one of the most important parameters in the synthesis of single crystal lithium rich materials, and it is necessary to determine the optimal calcination temperature to achieve a balance between capacity and cycle stability

The development of high-capacity cathode materials is one of most effective strategies to improve the energy density of Li-ions batteries. Layered Li-rich manganese-based cathode materials have attracted much attention from researchers due to their ultra-high specific capacity (>250 mAh g^-1) and low cost. However, Li-rich cathode materials still suffer some drawbacks especially its poor cycle performance due to the necessary high operation voltage, which accelerated structural deterioration toward inactive rock-salt phase and greatly limit their practical application. Herein, Ru are proposed as a doping element to substitute TM in Li_1.2Ni_0.2Mn_0.6O₂ as a prototype materials. The XRD and EDS mapping results indicate the Ru has been successfully doped in the layered structure and can restrain the structural degradation during cycling. The electrochemical tests present that the Li-rich cathode with 1 wt% Ru-doping displays the highest capacity retention with nearly no capacity fade over 40 cycles. This work should shed light on the structural and electrochemical enhancement of lithium-rich cathode materials for the most advanced Li ions batteries.

This paper firstly proposes an assessment index system for source-side integrated energy systems; secondly, the indexes are assigned weights through a combination of subjective and objective weighting methods by the set-value iterative method and the inverse entropy weighting method, and are evaluated using the object topology model; finally, an evaluation analysis is carried out in relation to a proposed source-side integrated energy system to verify the rationality of the proposed assessment index system and the effectiveness of the assessment method

Partial shading is one of the main causes of output power loss and module mismatch of photovoltaic(PV) arrays. This paper proposes a reconfiguration method based on an improved mayfly algorithm(IMA) for PV arrays with a total crosstied(TCT) topology. The shadows on the PV array are uniformly distributed by changing the electrical connections inside the PV array as a way to equalize the line currents of the PV array. Under the shadow of a 10×10 photovoltaic array, the IMA reconstruction method and Sudoku reconstruction method and other heuristic algorithms were compared respectively by two evaluation criteria of mismatch loss and power increase percentage, and the radiation distribution diagram of the photovoltaic array reconstructed by IMA was given. The results show that IMA can effectively improve the PV array output power and reduce the mismatch loss. The output PV curves all tend to show a single peak, and the reconfiguration results are all superior to the other methods. In addition, hardware-in-the-loop experiments on the RTLAB-based platform verify the hardware feasibility

Energy storage is an important supporting technology to stabilize the fluctuation of new energy, aggregate clean energy, and build a new power system. When configuring energy storage in photovoltaic power plants, how to balance technical, economic and environmental benefits is an urgent problem to be solved. This paper conducts research on energy storage optimization configuration technology including distributed photovoltaic power generation, combines planning and operation, and constructs a multi-objective energy storage location and capacity bi-level programming model that takes into account the uncertainty of photovoltaic power generation, using multi-objective particles The group algorithm is used to solve the optimal location and capacity power of energy storage. The IEEE-33 node system is used to simulate on MATLAB to verify the effectiveness of the proposed multi-objective energy storage double-level programming method.

In recent years, global environmental problems are becoming more and more prominent and serious, and non-renewable energy sources are continuously consumed with the development of society, so energy storage of renewable energy sources becomes especially important. Considering the problems of unstable, discontinuous and unfavorable use of renewable energy sources such as solar and wind energy, appropriate energy storage equipment is urgently needed to enable efficient use of the resources. Vanadium battery is a relatively mature liquid current battery with long life, high energy storage, easy maintenance, flexible design, green and other outstanding advantages, commonly used in renewable energy storage and smart grid peak shaving, with high economic value and development prospects. China is not only the world's largest vanadium ore resource country, but also the largest producer and consumer of vanadium, occupying an important position in the international vanadium market, and the rich vanadium ore resources have played a positive role in promoting the development and application of vanadium liquid flow batteries in China. This paper highlights the development status of vanadium liquid flow batteries, the distribution of vanadium ore resources, and makes relevant suggestions for the development of vanadium liquid flow battery industry.

With the rapid development of new power systems, energy storage, as an important flexible resource, needs to play an important role in the power market. As a power-limited resource, a reasonable dispatching model need to be built for energy storage. In this paper, the methods of energy storage participating in peak shaving market and their advantages and disadvantages are analyzed. A dispatching model considering multi-period coupling of energy storage participating in peak shaving market is constructed. Finally, an example is given to verify the beneficial effect of the model on the economy and safety of the system.

In response to the "Carbon Peak" and "Carbon Neutrality" initiatives, the National Energy Administration issued the "Notice on launching a national distributed photovoltaic pilot program", and distributed photovoltaic power stations have been booming. This paper designs a 600 kW distributed rooftop photovoltaic system, including the calculation and selection of photovoltaic modules, photovoltaic combination boxes, DC distribution cabinets, grid-connected inverters and other balance of system. The photovoltaic system consists of 1500 photovoltaic modules, 18 junction boxes, 6 DC power distribution cabinets, 6 grid-connected inverters and balance of system. The system finally connects to the 380V power grid at the user side.

Integrated energy system (IES) is significant to achieve the goal of carbon peak and carbon neutrality. IES is composed of multiple interconnected micro energy grids, with obvious distributed and multi-agent characteristics, and it is very difficult to operate efficiently and cooperatively. This paper proposed a distributed optimization strategy considering stepped carbon trading mechanism. Firstly, the mathematical model of interconnection operation of multi-energy microgrid is established by considering energy cost and carbon trading cost. Then, the distributed solution of the model is realized based on the globally consistent ADMM algorithm. Finally, the optimization results of the model are analysed by a typical case to verify that the proposed strategy can effectively realize the economic and low-carbon operation of the system.

Recently, solar energy has been developed and widely utilized in photovoltaic system networks and provided a significant clean energy supply for the world. Additionally, the photovoltaic system is an essential module to generate the electrical power in energy supplement systems. However, existing photovoltaic systems are vulnerable to the affection of climate environment and may cause uncountable economic loss and decrease the power generation for power supply system. Therefore, the development for faults recovery strategies are necessary in photovoltaic system. Existing methods are concentrated on utilizing the units control detection method to identify the faults points and cause numerous identification costs. In this paper, we utilize the long short-term memory network (LSTM) to achieve photovoltaic system faults detection task, which can enhance the detection accuracy and reduce the computation cost for identifying procedure. From our extensive experiment results, we can conclude that our proposed method achieves more than 90% detection accuracy with reasonable detection costs through comparing with existing detection methods.

The study aims to establish a model formula for the correlation between turbidity and suspended solids (SS) in coal mine water (CMW) with different concentration gradients. The formula's accuracy is then verified by testing five actual samples. The study obtained two conversion formulas for turbidity and SS, depending on the suspended matter content. The correlation curve between turbidity and SS is y=0.9336x-3.1393, R2=0.9986 when the suspended matter content is 0-200mg/L. When the suspended matter content is 200-600mg/L, the correlation curve between turbidity and SS is y=0.5314x+94.421, R2=0.9905. The study also compared the measured values of SS in other coal mines with the calculated values obtained from the formula and carried out an error analysis. The error between the measured values and calculated values is controlled within 4% when the suspended matter content is less than 200mg/L and within 5% when the suspended matter content is between 200-600mg/L. Considering the unavoidable error factors, the model formula can be helpful to the efficiently detect SS in actual CMW. The study is crucial as it provides an accurate and efficient method to determine the concentration of SS in CMW, which is essential for effective wastewater treatment and environmental protection.

Given the deep oil reservoirs in the Yanchang Formation in the western region of Yanchang Oilfield, the research on drilling and completion technologies for long open-hole horizontal wells is carried out because of the problems such as high drag and torque, difficult trajectory control, borehole cleaning, cementing quality, and many downhole accidents during drilling. From the perspective of drilling and completion, we have developed trajectory control, wellbore stabilization, borehole cleaning, and cementing completion technology for long open-hole horizontal wells. This technology achieved good results in WP-H47 well: Compared with the horizontal wells of the same period, the downhole complications during drilling and completion were significantly reduced and the average drilling cycle was shortened by 6.5 days. This technology helped build the longest open-hole horizontal well in the Yanchang Oilfield, which is a reference for the development of long open-hole horizontal wells in later years.

The reservoir in the B# oil region of Wuqi oilfield is relatively single, and in recent years, horizontal wells have been used for large-scale development due to ground constraints and other factors. In order to better improve the efficiency of geologic steering of horizontal wells and the drilling ratio of an oil reservoir, the preliminary 3D geological model is established, and the geological model is continuously revised by applying the drilling gamma, which combines the model and geologic steering to optimize the trajectory of horizontal wells and improve the accuracy of window entry to ensure the efficient and stable production of horizontal wells in the later stage. By applying 3D geological modeling and geologic steering technology to HW8 well, the drilling ratio of this well reached 91.1%, and the well was put into production by staged fracturing with a quick-drilling bridge plug, with good development effect. It is of important guidance and reference for future horizontal well development deployment and geologic steering in this area.

The abundant and idle roof resources in rural areas provide a good prerequisite for the promotion and construction of household photovoltaic (PV). Based on this background, this paper combs and analyzes the operation modes of rural household PV system, constructs a cost-benefit calculation model of household PV, calculates the economic benefits of four household PV operation modes through calculation examples. The research results can provide reference for economic evaluation and promotion of rural household PV system investment.

The distribution system's ability to operate securely and steadily will be seriously hampered by the enormous number of dispersed PV accesses present in the network. Large-scale access to energy storage systems in distribution networks has emerged as a development trend and a crucial means of achieving the large-scale grid connection of distributed PV due to the ongoing advancement of energy storage technologies. Currently, the biggest barrier preventing the use of energy storage systems in distribution networks is their high investment costs. This study examines the most cost-effective approach for allocating energy storage in a distributed PV system. With restrictions like upper and lower rated capacity constraints, installation location constraints, grid node power balance constraints, node voltage magnitude constraints, and branch active power constraints, the upper layer targets the investment and operation cost of distributed energy storage systems. The minimum PV abandonment rate is used as the optimization objective in the lower layer, and the constraints include the generator output constraint, storage charge state continuity constraint, storage charge state constraint, storage system power constraint, and storage system charge and discharge power constraint. Finally, the IEEE 33-node distribution network arithmetic example is used to demonstrate the usefulness and viability of the suggested approach. The simulation results also demonstrate how taking into account a specific abandonment rate can lower the investment and operating costs of an energy storage system.

As large-scale renewable energy sources are connected to the power system, the scale of centralized renewable energy sources stations is constantly expanding. More and more attention is paid to the problem of collaborative frequency control among power sources with different response capabilities. The droop control response mechanism that mimics the synchrotron design lacks flexibility. The emerging deep reinforcement learning method provides a new idea for the frequency control of renewable energy-based power systems. In this paper, a mathematical model describing the response characteristics of each power supply and the nonlinear characteristics of dead zones and limiting zones is constructed via mechanism analysis. In addition, an optimization objective function considering the frequency modulation performance and economy of renewable energy supplies is proposed, and the control strategy optimization problem is solved based on the twin-delayed deep deterministic policy gradient algorithm. Case studies show that the proposed method can effectively improve the transient frequency characteristics of the system and realize the cooperative participation of renewable energy sources with different dynamic characteristics with fast frequency responses.

With the goals of "carbon peaking" and "carbon neutrality", a large number of comprehensive energy systems with new energy as the theme have begun to be built, and research on the integrated system of "solar storage, charging and discharging" has been carried out based on photovoltaic consumption demand. First, establish an integrated system architecture of optical storage, charging and discharging. Secondly, the charging demand of electric vehicles is analyzed, and the photovoltaic power generation system and energy storage system are analyzed and modeled. Thirdly, the economic regulation model of the integrated system of solar storage, charging and discharging is established with carbon emissions as the penalty cost, and the improved whale algorithm is used to solve it. Finally, the feasibility and effectiveness of the model are verified by case analysis.

This paper decomposes the normalised and pre-processed raw PV data into different components by the quadratic frequency domain decomposition method, and then uses the random forest algorithm and the GRU prediction neural network optimised by the genetic algorithm to process the different components, and then reconstructs the results of the different prediction methods to obtain a short-term PV forecasting model. The improved quadratic frequency domain decomposition forecasting model is applied to the forecasting problem and compared with the traditional forecasting method, and the improved quadratic frequency domain decomposition forecasting model is proved to obtain more accurate forecasting results.

A cutter optimization method based on network energy and reduction of unit regulation cost is proposed for the problem of power support provided by wind and PV in system emergency control. Firstly, the branch stability measure function and power sensitivity are used to select suitable units for scheduling. Then, a cutter model is constructed to minimize system action costs by analyzing the cost of regulation. The response of each unit is obtained by solving the linear programming problem. Finally, the validity of the proposed method is verified by example analysis. The algorithm verifies that the proposed method can effectively reduce the economic cost of system regulation measures, fully exploit the power support capability of renewable energy units, and provide a new idea to deal with the transient safety problems brought by large-scale grid connection of new energy in the future.

In recent years, benefiting from the favorable policies such as the reduction of PV power generation costs and the "promotion of the whole county", distributed generation shows an exponential growth trend. Distributed generation does not have the ability to self-balance and require support from large power grids such as peak shaving, frequency regulation, voltage regulation, and accident backup. Promotion of distributed trading of photovoltaic not only fails to save investment from higher-level power grids, but also increases the demand for investment in capacity expansion and renovation of higher-level power grids due to the demand for surplus power grid connection. This paper proposed a new theoretical model to evaluate transmission-distribution price reasonably. Empirical results based on China data shows a reverse correlation between the transmission-distribution price and the return on distributed photovoltaic investment, which means the government should be cautious on balancing the investment of power grid companies and the profits of photovoltaic enterprises.

Taking a photovoltaic power plant as an example, a large-span suspension photovoltaic bracket is established in accordance with the requirements of the code and optimized. By adjusting the cable specifications and pre-tensioning force of the cable, multiple comparison models are established, and the comparison results of different models’ natural vibration periods, cable tension and maximum displacement are obtained through analysis and calculation to explore the optimal cable cross-sectional size and pre-tensioning force of the bracket. The aim is to draw relevant conclusions and provide reference for the design and optimization of similar continuous large-span suspension photovoltaic brackets.

The article establishes a integrated energy system optimization and scheduling model that considers the participation of P2G in the carbon trading market. Firstly, a integrated energy system operation framework was established based on the system load characteristics; Secondly, considering the background of carbon trading mechanism and the incentive effect of P2G on carbon trading mechanism, a comprehensive carbon trading cost model is established; Finally, a low-carbon scheduling model for the integrated energy system was established with the objective functions of minimizing operating costs and maximizing new energy consumption, and the effectiveness of the proposed model was verified through scenario comparison. Research has found that considering the comprehensive carbon trading cost model, setting a reasonable carbon price can effectively promote the low-carbon economic operation of the system.

This study examines the use of liquid cooling systems and phase change materials (PCMs) to control the temperature of lithium iron phosphate battery packs. The objective is to satisfy the 5C battery pack's heat dissipation requirements. The impacts of several factors, such as phase change temperatures, liquid flow rates, and delayed cooling start times, on the battery pack's temperature are examined using commercial CFD code. The findings of the study show that the battery pack's temperature dropping is slowed down with an earlier start of liquid cooling. Also, the temperature differential and rise of the battery gradually decrease as the cooling liquid's flow rate increases.

The running loss SHED was selected to study the impact of temperature on the running loss emission for light-duty vehicles, and the monthly evolution of vehicle running loss emissions was analyzed in consideration with calculation model, meteorological temperature, and travel patterns. The test results show that the running loss emissions of the vehicle gradually increased with the increase of temperature. In the CLTC cycle conditions, the emission in the low speed phase account for the highest emissions, and is mostly affected by temperature with the highest emission increment due to the accumulation of volatile or permeable fuel vapor before the running loss test caused by long-time hot soak and the fuel tank heating. Based on the meteorological temperature of Tianjin from January to December, the average annual mileage of motor vehicles, the monthly variation coefficient, and the number of motor vehicle, the annual running loss emission is estimated to be approximately 159.54 tons. The monthly running loss emission in January and February are relatively low, then gradually increase and reach the peak at June, stabilize from June to August, and gradually decrease from September to December.

The Internet of Energy and low-carbon electricity are now heavily reliant on integrated energy systems, which are capable of conserving energy and decreasing emissions. This architecture, founded on the carbon trading model, is comprised of an electric-heat-gas combined power source, comprising electric-gas and gas turbines.Firstly a step model is formed to determine the expense of carbon trading by dividing the carbon emission interval, and the dispatching model of the system is augmented with a carbon trading mechanism. Secondly, we developed a low-carbon economic planning model for electricity-heat-gas distribution systems, taking into account the costs of carbon trading and outsourced energy. An assessment of the sequencing models' outcomes, taking into consideration the system's low carbon content and economy, was conducted to evaluate the proposed model's efficacy

With the increasing development of low-carbon economy, the coupling degree of electric thermal gas system is deepening day by day. The operation mode of traditional energy supply system using electric heating separation mode and hierarchical dispatching of transmission and distribution network has been difficult to mine the whole network resources and realize the global optimal operation strategy. In order to increase the consumption of new energy and solve the multi-energy current coupling system, this paper puts forward a layered optimization operation strategy for the electric-thermal multi-energy current coupling system with Concentrated solar power (CSP) plant and carbon trading mechanism. The upper layer is the solution layer of multi-energy coupled flow system. In order to solve the matrix values of complex multi-energy coupled flow system, Newton's method, improved Newton's method and improved second-order cone collaborative solution methods are proposed. The lower layer is the multi-energy flow optimization layer. According to the system solution values obtained from the upper layer, the lower layer optimization aims at economic minimization of the total user cost. The carbon trading mechanism model is introduced to optimize the time sequence output of the electric thermal unit under different scenarios, and the mixed integer linear programming method is adopted. Finally, the validity is verified by a simulation example. The results show that this method can optimize the operation of the system, improve the accuracy and rapidity of the system, and "carbon trading and CSP power station" method can better constrain the carbon emission of the multi-energy flow coupling system, reduce the energy pressure of the device, and improve the economy of the total cost of the user.

The integration of an energy system in a district boosts the economics of energy supply and environmental protection by dispatching various energy devices; however, the combination of multiple energy sources increases the complexity of the system's operation, thus necessitating effective optimisation to guarantee its operational economy. In this paper, On the basis of optimal operational economics, a resource allocation model for a district integrated energy system is created, and the proposed optimal resource allocation scheme is applied to a case study area. The findings indicate that the district integrated energy system's operating economics can be improved by connecting several energy stations, which verifies the viability of the method proposed in this research.

With the rapid development of integrated circuits, the number of electronic components on PCBs has multiplied. The increase in the number of components has led to more factors affecting the stability of PCB, with temperature being one of them. Excessive temperature can lead to functional failure or burnout of electronic components. A hybrid algorithm based on particle swarm optimization and simulated annealing algorithm is proposed to optimize the thermal layout of PCB electronic components. The article analyzes the principle and algorithm improvement process of thermal layout optimization, provides specific optimization cases, and verifies the feasibility of this method through MATLAB simulation experiments.

Energy storage projects have a broad application background in the electric power system due to their particularity of separating the production and consumption of electric energy from time and space. As one of the key technologies to support a high proportion of renewable energy system, the scientific evaluation method is an important basis to measure its value. This paper constructs a planning model of energy storage project based on multi-dimensional value power generation enterprises. The method model first selects primary indexes from economy, reliability and green development; then screens and reduces the redundant indexes based on the variation characteristics and collaborative coupling degree between indexes; constructs intuitive judgment matrix, calculates the comprehensive weight coefficient; and finally calculates the system value of different energy storage configuration schemes. The maximum system value is the best energy storage project planning and configuration scheme.

Strain clamps of transmission lines are the main part of transmission lines, which play the role of bearing tensile force and transmitting current in the transmission line. In this paper, we focus on the distance between the end of steel anchor and strand in the strain clamps. We propose a method using YOLOv4 and classic image processing methods to measure the distance referred to above, according to which we can judge whether the strain clamps have defects about distance. We first got the bounding boxes of the end of steel anchor and strand in the strain clamps by YOLOv4, thus calculating the distance between the two bounding boxes. Meanwhile, we used classic image processing methods to get the diameter of the steel anchor. Finally, we calculated the ratio of the two distances mentioned above to determine whether the defect exists. We achieve good performance in the detection of distance defects in strain clamps with high accuracy through the research in this paper.

Redundant execution technology is one of the effective ways to improve the safety and reliability of computer systems. By rationally configuring redundant resources, adding components with the same function, using the determined redundant execution logic to coordinate and efficiently execute synchronously can effectively ensure high availability of the machine and system. At the same time, dynamic heterogeneous redundancy technology is also a key technology for mimic defense. Enumerates the implementations of dynamic redundancy at thread level (And the combination with the hardware level). The advantages and disadvantages of different implementations are analyzed.

Nowadays, with the rapid development of automotive electronics, ECC, as a functional safety mechanism with high reliability, has become increasingly important in the design of automotive chips. Based on the Hsiao code and parallel algorithm, the Verilog HDL is adopted to design the ECC IP core supporting the data coding and decoding within 247bit, and the verification platform is established based on the UVM for functional verification. The verification results show that the ECC IP core can achieve the error correction of data 1bit and error detection of over 2bit, and can expand the coding bit width to achieve multi-scene reuse. The functional coverage ratio and code coverage ratio reach 100%.

The basic structure of the CX pantograph and lifting and lowering pantograph function is introduced. It analyzes two typical faults, such as abnormal deviation and poor receiving flow, which are found in the daily operation and maintenance of CX pantograph, to clarify the deep-level causes of pantograph faults, and explore the influence of geometric and mechanical characteristics of pantograph on the relationship between pantograph and network. Then, relevant suggestions on daily operation maintenance and technical transformation of CX pantograph are put forward to provide technical support for improving the operation safety of EMU pantograph and improving the relationship between pantograph and network.

Recently, data in enterprises is showing an explosive trend, and many companies have begun to build or use data centers for unified management and mining of data. However, the visualization and analysis of data in China and Taiwan has become a common problem in the industry. To deal with massive high-dimensional data, traditional visualization methods are difficult to find the potential connection of data. Therefore, this paper proposes a data dimensionality reduction visualization method based on principal component analysis, and the method has a good application prospect in the big data processing of energy and electricity. The experimental results demonstrate that the suggested method can effectively integrate the data features and visually analyze the data.

This study considers an optimal design of an island stand-alone microgrid under uncertainty, particularly facing the extreme events. To balance the environment, cost, and the reliability of the system, we propose a novel model of an island microgrid with considering multi-group distributed generators. In the proposed model, multi-group distributed wind turbine, distributed photovoltaic, distributed diesel engine and distributed battery energy storage system are coordinated to reduce the total cost and pollutant emissions, and ensure the reliability. Experimental results show that the developed island microgrid is far more reliable than the island microgrid obtained from traditional model while facing the extreme events.

The layout of urban charging stations is unreasonable, and it is difficult to charge green energy vehicles. Based on the above problems, the equalization, accessibility, allocation and construction rate of Tianjin Automobile Charging Station are studied. Firstly, the hot data of important places, charging stations and parking lots are obtained in Tianjin through Gaode API, then ArcGIS kernel density analysis, buffer analysis and other methods are used to study. The study found that the layout of automobile charging stations in Tianjin is relatively balanced, and the core area of Tianjin has achieved full coverage of automobile charging stations. When the service radius is 5 km, the relative coverage rate of the six outer suburbs is relatively low, of which Baodi District and Jizhou District have the lowest coverage rate, not exceeding 50%. When the service radius is 3 km, the coverage rate of the four districts around the city does not reach 90%, and the relative coverage rate of the six districts in the outer suburbs, except Wuqing District, is 50.43%, does not reach 50%. The allocation rate of charging stations in the parking lot is only 3.41%. Therefore, Tianjin should strengthen the construction of automobile charging stations, especially in the surrounding areas of the city. The construction of automobile charging stations should be expanded according to the regional hot spot demand analysis to meet the automobile charging needs of the general public.

Phase change material (PCM) has high energy storage density, but the freezing rate decreases with the increase of frozen thickness. Foam freezing can sharply increase the freezing rate. In this research, the effects of bubble radius and convective heat transfer coefficient on freezing process were discussed. It was found that with the decrease of bubble radius and the increase of convective heat transfer coefficient, the shape of instantaneous freezing rate curve changed from linear dominant to C-type and then to L-type. With the increase of convective heat transfer coefficient, the opening direction of C-type curve of foam outlet temperature changed reversely, and the curves all intersected a specific area. The convective heat transfer coefficient was near the critical point of natural convection and light forced convection.

In this paper, by studying the sensitivity of reliability indicators to the scale of electric vehicles, a new perspective can be found to quantitatively assess the electric vehicle acceptance capacity. Firstly, an EV charging load model and an optimization strategy for orderly peak and valley tariff periods are established; secondly, indicators such as maximum acceptance and reliability sensitivity are proposed to quantitatively characterize the system's ability to withstand EV access; finally, a quantitative assessment method for EV acceptance taking into account orderly charging is proposed in combination with traditional reliability assessment methods, and a significant sampling method is used to improve the sampling simulation efficiency. The quantitative assessment method and metrics can be extended and applied to the actual grid regulation of EV access scale to ensure the safety and reliability of the system.

When large-scale distributed generation (DG) is connected to the grid, the net load of the distribution system fluctuates due to its output characteristics. The system power flow presents a strong random characteristic, which will lead to the phenomenon of reverse power transmission and bidirectional power flow. The traditional scheme of increasing distribution transformer and line reconstruction is difficult to collect data and ensure accuracy, and the calculation workload is large, time-consuming, and labor-intensive. Therefore, in the current situation, priority should be given to the use of distributed energy storage, static var generators, and other plug-and-play devices that are easy to deploy, as well as existing household photovoltaic power generation sources to participate in voltage and line loss management. In this paper, a line loss detection scheme based on distributed photovoltaic access grid is put. In this scheme, a set of joint optimization models of the planning layer and optimization layer is constructed, which takes network topology and line specifications as static parameter input. It also takes a typical photovoltaic-load power generation scenario of intelligent learning mode as dynamic parameter input. Besides, it integrates active and reactive power resources such as distributed energy storage, static var generator, and photovoltaic power system, and integrates the planning layer and operation layer. To synchronously solve the location and capacity scheme of the equipment planning layer and the optimal control strategy of the operation layer, it is helpful to promote a more accurate measurement of electric energy and has an incentive effect on both supply and demand sides to suppress power harmonics and improve the utilization rate of electric energy.

To discuss the differences between conducting WLTC, partial WLTC (consisting of Low, Medium, and High), and CLTCP in the energy consumption test of electric vehicles, this study compares the characteristics of WLTC, partial WLTC, and CLTC-P, including running characteristics, energy demand, regenerative energy, then conducts energy consumption test to verify the theoretical analysis. The results show that the running characteristics of the partial WLTC, WLTC, and CLTCP test cycles are significantly different. The average speed, maximum average acceleration, and deceleration of partial WLTC are higher than CLTC-P, and the idle ratio is lower than CLTC-P. According to energy consumption test results, the energy consumption under WLTC is 25.9% higher than CLTC-P, and the energy consumption under partial WLTC is still higher than CLTC-P, with a difference of 4.5%.

The accurate prediction of the charging load of electric buses is helpful to the rational dispatching of the power grid. Therefore, this paper proposes a daily load curve forecasting method for electric bus clusters. First, a two-scale similarity measurement method for load curve is proposed, and the improved spectral clustering algorithm is used to cluster the daily load curve. Secondly, considering the influence factors such as temperature and season of each type of load, the improved Long Short-Term Memory (LSTM) is used to predict the charging load of each type of bus, and the total charging load on the day to be predicted is obtained by summing the prediction results of each type. Finally, a case study is carried out with the actual data of a city in East China. Compared with other prediction methods, the proposed method shows higher accuracy.

Hydrogen as a clean energy carrier is of great potential to store energy coming from renewable and intermittent power sources and proton exchange membrane mater electrolysis (PEMWE) is one of the most desired technology for high purity hydrogen production. However, the electrolytic performance of PEMWE is limited by several factors and more efforts should be paid to improve the performance. In this paper, a nonpressurized PEMWE was constructed and the effects of key influencing factors (inlet water temperature, water flow rate, assembly pressure, porous transport layer type and proton exchange membrane type) on electrolytic performance were experimentally investigated. The results show that a good pressure distribution was found inside the electrolyzer. The optimal inlet water temperature of 80 oC and flowrate of 90 sccm can meet the requirement of mass transfer at rated current density. At 1 atm, the assembly torque of 2 Nm can achieve effective sealing while extra torque is needed for high-pressure working conditions. Durability test over 60 h indicated that a good durability of the nonpressurized PEMWE under the above conditions. In addition, the electrolytic performance could be also improved by using titanium felt as anode/cathode PTL and decreasing the thickness of PEM in a certain range.

At present, the lightning protection structure of photovoltaic power station buildings is mostly a one-way structure. The lightning protection area is not fixed, which leads to an increase in impulse grounding resistance. Therefore, the analysis and verification of the lightning protection design of the photovoltaic power station building are proposed. Based on the actual measurement requirements and standards, the vulnerable parts of the photovoltaic power station are calibrated first. The lightning induction points are set. On this basis, the multi-objective form is adopted to define the specific lightning protection area inside the building. The multi-objective lightning dynamic protection structure is constructed, and the grounding treatment is used to realize the lightning protection of the power station building. The final test results indicate that the impulse grounding resistance decreases gradually with the expansion of the protection area, which verifies that the lightning protection effect has been improved. Besides, the lightning protection structure is more controllable, which has practical application value.

The comprehensive efficiency of photovoltaic power generation system is only a little more than 10% while the efficiency of inverter is considered, in order to maximize the use of solar energy, it is especially necessary to adjust the working point of photovoltaic array in real time, so that it always works near the maximum power point. Photovoltaic power generation system is a very nonlinear system, its mathematical model is complex and it is difficult to get its accurate model. In this paper, to avoid the internal complexity of photovoltaic power generation module, the RBF neural network identification technology is used to establish the nonlinear MPPT control model of photovoltaic power generation module, and the control effect of the MPPT controller is verified by simulation. It is compared with the traditional perturbation observation control method. The simulation results show that it is feasible to use RBF neural network to establish the nonlinear model of photovoltaic power generation module under complex lighting conditions, and the designed RBF neural network MPPT controller is effective, which can be used to predict the MPPT response of photovoltaic power generation module online, and its performance is much better than that of traditional controllers based on disturbance observation method.

When insulation defects occur inside SF₆ electrical equipment, the SF₆ gas inside the equipment may decompose to produce SF₆ decomposition components. SO₂ is one of the main decomposition components of SF₆ decomposition components. The tunable diode laser absorption spectroscopy technique is characterized by a simple experimental system and high capacity for accurate multi-parameter simultaneous measurements, but the process of detecting SO₂ will be interfered by other SF₆ decomposition components. Therefore, avoiding the interference of other SF₆ decomposition components and accurately detecting the SO₂ concentration is important for the assessment of the internal insulation status of electrical equipment. In this paper, the infrared spectra of H₂S, SF₆ and SO₂ were analyzed by the spectral database, and it was found that SO₂ has a high infrared absorption peak in the 1300~1400 cm-1 band and is less interfered by H₂S and SF₆. Therefore, the 1300~1400 cm-1 band was chosen for the experiment. TDLAS technology was used to build the experimental platform. Use 50 μL/L H2S and pure SF₆ as interfering gas, the average value of the second harmonic peak corresponding to different concentrations of SO₂ is detected. The actual concentration of SO₂ is brought into the fitted relational equation of the second harmonic peak average of SO₂ gas for inversion calculation. The inverse results of the average value of the second harmonic peak of SO₂ at different concentrations under the interference of SF6 and H2S are obtained. Experimental results show that under SF₆ interference, the R₂ of SO₂ concentration and second harmonic peak reaches 0.995 with a maximum error of 0.5%; Under the H₂S interference, the R₂ of SO₂ concentration and the average value of the second harmonic peak reached 0.993, with a maximum error of 2.43%. The research results provide a technical reference and support for the effective detection of trace SO₂.

Indoor airflow distribution plays a very important role in the health of personnel and environmental quality, and floor air supply, as a new form of air supply, is widely concerned because of its characteristics of improving air quality in the work area, energy saving and environmental protection, etc. In this paper, a vertical temperature measurement device was established in the floor air supply laboratory, and the distribution of measurement points was selected from 0.1 m, 0.3 m, 0.6 m, 0.9 m, 1.1 m, 1.3 m, 1.7 m, and By comparing the overall indoor temperature size, indoor vertical temperature gradient, thermal stratification height, air supply temperature, indoor thermal load, air supply volume, we obtain the characteristics of the influence of air supply parameters and environmental parameters on the indoor vertical temperature distribution of the downdraft and downdraft ventilation system.

Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is of great significance to practical production life. In recent years, the data-driven RUL prediction method based on machine learning has become a popular research topic both domestically and internationally. However, a significant number of research works fail to consider the connection between fore-and-aft data, leading to inadequate accuracy of RUL prediction or excessive model complexity. To address this issue, we propose a hybrid deep learning method, named CNN-GRU-DNN, which combines the Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and deep neural network (DNN). The CNN-GRU-DNN fusion model can deeply explore the high-dimensional features and long-term dependencies in the time series data of lithium-ion batteries, and effectively leverage the connection between fore-and-after data to enhance RUL prediction accuracy. The CNN hyperparameters are obtained through simulation, and five feature parameters with high correlation, including capacity, equal voltage drop discharge time, average discharge temperature, average discharge voltage, etc., are selected as input features of the CNN-GRU-DNN fusion model. Experiments are conducted on the NASA lithium-ion battery aging dataset using three statistical indicators, namely MAE, R², and RMSE, and numerical values are used to evaluate the prediction results. Comparative analysis with other hybrid methods (CNN-GRU), single neural network algorithms (CNN, GRU), and various machine learning (ML) algorithms demonstrates the superiority of the proposed CNN-GRU-DNN fusion model in terms of RUL prediction accuracy. This provides a high reference value for the study of high-accuracy prediction of RUL of lithium-ion batteries. In conclusion, the proposed CNN-GRU-DNN fusion model effectively captures the complex relationship between the high-dimensional features and long-term dependencies of time series data of lithium-ion batteries, and provides an accurate prediction. This study offers valuable insights into the development of high-precision RUL prediction methods using machine learning techniques.

With the rapid development of high-speed railways, power quality problems such as negative sequence current, harmonics, and energy consumption in traction power supply systems have become increasingly prominent. Using Railway Power Conditioner (RPC) to control power quality is a popular scheme to solve these problems. However, traditional RPC usually requires two step-down transformers and has the problems of a large footprint, low voltage level, and inability to use regenerative braking energy fully. In this paper, we adopt the RPC composed of the Modular Multilevel Converter (MMC) to solve these problems of traditional RPC. Besides, we integrate a supercapacitor energy storage unit on the SubModule (SM) of the MMC, which makes up for the shortcomings of traditional RPC and recycles regenerative braking energy.

In the face of the change of light conditions of local photovoltaic cells in the photovoltaic array, the P-V curve of photovoltaic cells shows the characteristics of multi-peak. Using Perturb and Observe algorithms (P&O), the traditional MPPT (Maximum Power Point Tracking) Perturb and observe algorithms (P&O) is easy to stay in the local optimal state, which results in the loss of power conversion efficiency of photovoltaic power generation system. In order to improve energy efficiency, the search speed and global optimal accuracy of maximum power point tracking are improved. Aiming at the multi-peak P-V curve caused by local illumination changes, the Improved Atom Search optimization (IASO) MPPT algorithm is proposed. The experimental simulation design of a variety of partial photovoltaic cells under different light intensity and light changes, using the IASO algorithm to compare with ASO, P&O. Simulation results show that the proposed algorithm has lower power fluctuation, higher stability, faster tracking speed and higher efficiency.

An improved AUKF algorithm is proposed based on the adaptive traceless Kalman filter (AUKF), and the main factors that affect SOC estimation are parametrically corrected in the algorithm. The goal of this research is to be able to accurately estimate the battery state of charge (SOC) of lithium batteries. In order to do this, the main factors that affect the accuracy of battery SOC estimation as well as the advantages and disadvantages of traditional battery SOC estimation are considered. The algorithm is derived from the second-order RC equivalent circuit model of the battery. To obtain the improved AUKF algorithm, the output deviation covariance of each measurement is taken as the noise covariance. This allows the noise covariance to be updated with time and eliminates the issue that the noise covariance is a constant source of error. The results of the experiments demonstrate that an upgraded version of the AUKF algorithm may provide an accurate estimation of the battery SOC value.

This article first briefly introduces the control principle of flexible DC transmission, and focuses on the analysis of the dual-loop vector control used in the simulation model of this article. Then, based on the existing knowledge, the simulation model was built in two cases: namely, the DC output after the multi-machine AC integration and the single-machine DC output. In the first model, an ideal AC voltage source is used to simulate the AC power generated by the wind farm after the collection, and then the rectifier part is connected, and then the DC-DC boost is directly performed, and finally the load is connected to the DC high-voltage output side. The output power can be controlled by the load; in the second model, a permanent magnet synchronous motor is used to simulate the output of AC power instead of the fan. After that, the rectifier part is connected to the first model, and then the DC-DC boost is directly performed, and finally the output side is directly connected Ideal DC power supply. The ideal DC power supply here is equivalent to an infinite ideal DC power grid.

With more and more attention being paid to energy and environment, electric vehicles have ushered in a new era. Batteries have been widely adopted as power source because of its superior performance and low-pollution characteristics is one of main factors affecting battery performance. Heat accumulation will have a serious bad influence on the life and performance of the battery, and may even result in the burning or explosion of the battery. In this paper, based on the heat generation mechanism of battery, a three-dimensional model of 18650 Battery at 27℃ is established, subsequently, a module of battery pack of electric vehicle is selected as the research object, and the existing serpentine water cooling plate is adopted to improve the structure, thus ensuring the battery work in the best working temperature range and simultaneously improving the temperature homogeneity of the pack as much as possible. According to the results, the average surface temperature of the pack is 42.87℃and the maximum temperature difference of the pack is 4.82℃ when the reverse flow rate is 2m/s and battery coverage angle is 140°and 220° on the battery side in low and high temperature area. The heat dissipation performance of the battery can be enhanced and the temperature homogeneity of the pack can be improved by changing the cold plate’s covering angle reasonably.

Electric Fast Transient Burst (EFTB) is a type of electrical disturbance that occurs in electronic circuits and devices. Such as automatic coal mining control system. EFTB is characterized by its short duration, high amplitude, and high-frequency content, which can cause malfunction and damage in electronic systems. EFTB can be generated by various sources such as lightning strikes, electrostatic discharge, power surges, and switching operations. In recent years, the increasing complexity and sensitivity of electronic systems have made them more vulnerable to EFTB, making it essential to investigate the characteristics and behavior of EFTB to improve the reliability and safety of electronic systems. The main objective of this research is to investigate the characteristics and behavior of Electric Fast Transient Burst (EFTB) and its effects on electronic systems. Specifically, the research aims to: Analyze the EFTB waveform and its frequency spectrum, Evaluate the effects of EFTB on electronic components and systems, develop mitigation techniques to reduce the impact of EFTB on automatic coal mining control system.

With the expanding scale of photovoltaic (PV) power generation installed in the grid, the scattered communication terminals and the large-scale data communication led to the problem that the master station cannot aggregate information from multiple terminals and analyze data efficiently in real time. In this paper, a power line communication (PLC)-based efficient information aggregation and data compression architecture is constructed. On the basis, we propose genetic algorithm-based information aggregation algorithm to optimize PLC channel selection and reduce information aggregation delay. Moreover, we propose a multi-intersection approximation data estimation algorithm to support the master station for regulation and decision-making by improving data compression accuracy. Simulation results show that the proposed algorithm has superior performance in terms of information aggregation delay and data compression accuracy.

With the adjustment of the energy structure system, the proportion of new energy generation such as wind power and photovoltaic (PV) is increasing, and the forecasting technology for new energy loads such as wind power and PV has become a common concern for grid development and planning. Unlike wind power generation, PV output is coupled with more factors, and its cyclicality and volatility are more significant. For PV power forecasting, the data mining analysis of the PV plant itself is more important than the forecasting algorithm, because the accuracy of the forecasting result is not only related to the model, but also depends on the variables input to the model. This paper proposes a correlation analysis of PV plant data to address the above issues. Firstly, the periodic and fluctuating characteristics of PV power generation are systematically and intuitively described from the PV plant data itself, then the data types of PV plants are described from three aspects: data sources, time scale and spatial scale. Finally, the Pearson correlation coefficient is used to correlate the data, and the impact of sampling granularity on PV output forecasting is pointed out, providing a theoretical basis for PV forecasting work

In order to promote the development of wind power, China has issued a subsidy policy for wind power. Due to the lack of research on the relationship between the scale of wind power development and the subsidy mechanism, the subsidy amount has become the financial burden of large wind power provinces and it is difficult to implement the subsidy, affecting the sustainable development of wind power. This paper analyses the subsidy mechanism to promote the scale development of wind power in the electricity market environment, and proposes Bass diffusion model integrating multiple subsidies. The optimal subsidy mechanism to promote the scale development of wind power is deduced by considering the total subsidy constraint and the scale development goal of wind power. The results show that the improved Bass diffusion model can quantify the impact of subsidies on the scale of wind power diffusion. Based on this, the proposed optimal comprehensive subsidy mechanism has a good role in promoting the scale development of wind power. At the same time, it can effectively control the subsidy gap, improve the subsidy efficiency, reduce the financial burden, and realize the sustainable scale development of wind power.

With the encouragement of new energy policies and the continuous promotion of solar energy development. Distributed photovoltaic (PV) grid connection operation has become a trend. However, due to the large impact of the weather, the power output of distributed PV is unstable. It is difficult for the grid scheduling department to timely adjust the power generation and load of the grid based on PV power changes, resulting in difficulty in balancing supply and demand, affecting the stable operation of the grid and PV consumption. In response to the above issues, this paper proposes a wavelet neural network-based distributed PV grid-connected power prediction method, which provides data reference for the scheduling department through the prediction of distributed PV power. First, distributed PV power prediction architecture is studied, including the sensing layer, prediction layer, and service layer. Then the specific functions of the prediction layer are designed based on a wavelet neural network. Finally, the simulation results show that the proposed method can effectively achieve the power prediction of distributed PV.

Conventional digital grid user side power carbon emission monitoring model mainly determines model parameters based on autoregressive condition heteroscedasticity, which is vulnerable to the lag effect of constraints, resulting in poor monitoring effect of the model. Therefore, a new digital grid user side power carbon emission monitoring model needs to be designed based on the cloud platform. That is to say, the cloud platform is used to obtain the monitoring data of power carbon emissions at the grid user side, and then combined with the carbon load active power relationship, the digital grid user side power carbon emissions monitoring mathematical model is constructed, which realizes effective carbon emissions monitoring. The case analysis results show that the designed carbon emission monitoring model on the user side of the digital power grid based on the cloud platform has a good monitoring effect, and the carbon emission intensity of each node monitored is more consistent with the actual carbon emission intensity, which proves that the designed model has good performance, reliability, and certain application value, and has made certain contributions to the development of subsequent grid operation schemes.

In order to improve the accuracy of short-term wind power prediction, an improved chameleon Swarm algorithm is proposed and applied to the wind power prediction model established in BiLSTM. Firstly, Circle chaotic mapping was used to initialize the population of the standard Chameleon Swarm algorithm, then spiral search in the whale algorithm was used to improve the global search ability and Levy flight strategy was introduced to improve the local development ability, and then Gaussian detection was used to improve the optimization ability. Finally, the Bilstm-based prediction model was established and the historical wind farm data was used for example analysis.

High frequency ripple is a common electromagnetic phenomenon in power system, which is caused by the instantaneous switch of power electronic equipment. Nowadays, lithium batteries are widely used, and the high-frequency ripple generated will not only interfere with the power system, but also affect other electronic devices and sensors, greatly increasing the risk of use. This paper studies the high frequency ripple generated in the working circuit of lithium battery. Firstly, the generation and detection methods of high frequency ripple are summarized, and then the filtering circuit for processing high frequency ripple is summarized. In this paper, the high frequency ripple is systematically analyzed, which is helpful for more people to understand the high frequency ripple, solve its problems, expand the research ideas, and provide direction for future research.

The conventional substation differential power supply zoning method uses the grey clustering coefficient to determine the power supply weight of each zone, which is vulnerable to the influence of the dynamic entropy value of power supply, leading to the high power supply load. Therefore, a new substation differential power supply zoning method needs to be designed based on grid division. That is, the grid division is used to predict the power supply load of substation partition, and then the differential power supply partition model of substation is constructed by combining the load characteristics complementation, thus realizing the differential power supply partition of substation. The case analysis results show that the designed substation differential power supply zoning method based on grid division has a small power supply load, which proves that the designed power supply zoning method has a good power supply effect, can reduce power supply loss, has a certain application value, and has made a certain contribution to reducing the regional power supply cost.

In this paper, the design of a 100kW commercial complex rooftop photovoltaic power generation system, photovoltaic module selected YL-260P-29b polycrystalline silicon cell, 23 series 17 parallel, inverter selected Solis-50K-US-F-LSW four-way input inverter. The inclination Angle of the photovoltaic square was designed to be 40°, and the azimuth was due south. Based on the calculation and simulation of pvsyst software, the power generation of the system in the first year was 153MWh. By setting the annual decay rate of the system, the replacement emission of carbon dioxide in 25 years was 2561.2 tons, it had good economic and environmental benefits.

The main purpose of this article is to introduce fuel cells and fuel cell engines. This article begins with an explanation of the beginnings and development of the fuel cell. It then discusses the advancements in modern science and technology that have led to the creation of various types of fuel cells, as well as their characteristics and advantages. For instance, fuel cells can be used in many ways because it only requires hydrogen and oxygen to transform chemical energy into electrical energy and helps to reduce pollution in the atmosphere. Moreover, the thermal efficiency of fuel cell engines is undoubtedly higher than that of gasoline and diesel engines. The following article illustrates how fuel cells might be utilized in contemporary life, including the benefits of fuel cells over conventional internal combustion engines. However, fuel cells also have many drawbacks, such as the fact that hydrogen is typically difficult to store at room temperature and pressure. Furthermore, fuel cells are typically not very long-lasting and are very expensive, both of which are factors that are considered when fuel cells are evaluated

Ammonia as an alternative fuel has raised more and more attention in recent years. To utilize ammonia co-firing with methane as potential fuel for power generation, studies need to be conducted for further industrial implementation. In this paper, the ignition delay time characteristics of ammonia as a fuel is studied. Different factors that affect the ignition delay time are investigated. Numerical simulations are carried out using detailed chemical kinetics mechanism. The influence of ammonia cracking rate, ammonia substitution rate and equivalence ratio on the ignition delay time are studied on methane/ammonia mixed fuel under wide range of conditions. The results can provide fundamental data for methane/ammonia combustions utilization.

At present, the transmission of information from the terminals in the low-voltage station area is generally through power line carrier communication, and the working environment of the collection terminals is complicated, which is prone to missing data due to interference in the channel and abnormal collection equipment. In this paper, we propose a method to repair the abnormal missing data of distribution and low-voltage measurement data for the service middle station, which constructs the electricity consumption data matrix through the massive user data in the data middle station and records the set of missing data in the electricity consumption data matrix; constructs a pre-population model with the objective function of minimizing the kernel parametrization of the matrix, and applies the singular value threshold algorithm to pre-populate the missing data in the middle electricity consumption data matrix; clusters the pre-populated The pre-populated electricity consumption data matrix is clustered, and a low-rank repair model of electricity consumption data with joint optimization of matrix kernel parametrization and L1 parametrization is established; by solving the low-rank repair model of electricity consumption data with joint optimization of matrix kernel parametrization and L1 parametrization, the secondary repaired electricity consumption data matrix is obtained. Finally, comparing this method with the traditional interpolation restoration method, this method can obtain higher accuracy restored data by low-rank matrix restoration and effectively improve the data quality of the information collection system in the distribution business middle station.

Currently, the detection of wind turbine blade damage mainly relies on regular plan-based maintenance and manual inspections. In this study, a method for extracting audio features and detecting damage in wind turbine blades with wavelet denoising is proposed. This method first uses wavelet denoising to process the original audio signal, the denoised audio is then split into frames with Hamming windowing function. After that, multi-scale features are extracted in both time and frequency domains. Principal component analysis is used to reduce the dimensionality of the features, and clustering canters are obtained through K-means clustering analysis. Finally, Gaussian distribution outlier detection is used to detect audio signals from damaged blades. Experimental results using lab-generated audio data show that the proposed method has high accuracy and strong robustness in detecting wind turbine blade damage.

A large number of distributed generation (DG) are connected to the distribution network, which is not only conducive to energy conservation and emission reduction and optimization of energy allocation, but also conducive to the rapid recovery of power supply in non-fault blackout areas after permanent failure of the distribution network. Under the premise of island division based on niche genetic algorithm (NGA), a fault recovery and reconstruction strategy of distribution network considering the results of island division is proposed. In the phase of fault recovery and reconstruction, the influence of island effect on reconstruction results is fully considered. A mathematical model based on the combined minimum of total power load loss, network loss and switching time is established, and the genetic algorithm is used to solve the model. Taking PG&E 69 node distribution system as an example, the simulation results verify the accuracy and practicability of the fault recovery and reconstruction method proposed in this paper.

In order to solve the problem of data islands, the power system needs to integrate data from other industries for crossdomain fusion modeling. This paper focuses on privacy computing technology, combined with the power industry's big data capabilities, and proposes a research method for cross-domain modeling. And practice this method from the three links of privacy computing technology architecture, federated learning fusion modeling, and industry model application, and finally summarize its innovative advantages and value enhancement points.

Edge control technologies are increasingly used in smart grids. Also distributed generation microgrid systems need edge control technology for fine-grained management of the system. This paper introduces the main functions of edge-cloud collaboration, proposes the edge control architecture for distributed generation microgrid, then elaborates the architecture establishment scheme including data processing, network communication, and implementation of security mechanism, and finally demonstrates that the edge control technology can be successfully applied to distributed generation microgrid system through the application in a distributed generation network on a sea island.

In view of the current situation in the daily operation and maintenance of the distribution network and the modern marketing management, the large number of distribution stations and scattered distribution of users, the poor consistency between the line and the user map, the large number of abnormal types, the few analysis tools and the heavy loss reduction task, the distributed load monitoring and abnormal diagnosis technology of the distribution network is comprehensively studied by using the system integration technology of "big, cloud, mobile, intelligence", and the distributed load monitoring terminal and micro-application APP of distribution network are developed to realize highprecision three-phase voltage, current and power. It has the functions of harmonic calculation, unbalance degree calculation, voltage deviation calculation, abnormal wiring diagnosis alarm, neutral line zero sequence current measurement, zero sequence current limit alarm and so on. The pilot application shows that the distributed load monitoring terminal of distribution network can realize the precise positioning of the topological relationship, operation situation, voltage quality and line loss of 10 kV and 0.4 kV lines and the precise positioning of measurable and abnormal situations, which can improve the operation and management level of distribution network for the first-line operation and maintenance team at the grass-roots level, which has great application value.

Due to the randomness and volatility of the distributed power supply, the output of the distributed power supply will change greatly, so that the voltage of each node of the distribution network line may exceed the limit. In order to make all parts of the network meet the prescribed standards and improve the power supply quality of the grid-connected generation system of the distributed power supply, this paper proposes an optimization method of 10kv distribution network configuration connected to the distributed power supply. The optimal access quantity, capacity and grid-connected position of distributed power supply are obtained by taking the minimum network loss and the node voltage not exceeding the limit as optimization objectives. Finally, IEEE33 nodes are used for simulation analysis. After optimization, the active power loss of the system is reduced by 56.87%, and the reactive power loss is reduced by 59.04%, which maximizes the operation mode of the system, greatly reduces the network loss of the system, and improves the economic benefits of the system to a certain extent.

In order to solve the problem of unreasonable distribution of coolant flow rate in fuel cell engines, resulting in excessive overall flow rate between the stack and the intercooler, a combination of experimental testing, simulation analysis, and theoretical calculation was used to optimize the flow distribution manifold. Experimental design was carried out to measure the rates of the coolant flow entering into the stacks and the intercooler at different pump speeds. Using a combination of CFD numerical simulation and mathematical modeling methods, the coolant flow rates were redistributed. Taking the distribution manifold structure of a certain fuel cell cooling system as an example, its structure was optimized to reduce the flow rate of the intercooler from 0.73 kg/s to 0.368 kg/s while ensuring sufficient flow rate to the stacks. This study gives some guidelines for the flow distribution design of similar systems in engineering applications.

In order to study the turn damage phenomenon of the six-pole armature, a three-dimensional model of the six-pole electromagnetic rail transmitter was firstly established, and the armature turn judgment model was built by the 1g/A rule; the electromagnetic-structural coupling was performed by using ANSYS finite element simulation software, and the electromagnetic force on the armature, the contact pressure on the armature arm and the deformation state of the armature under the occurrence of turn time were found out, and the occurrence of armature turn was judged.

Lightning impulse overvoltage is the main factor to determine the insulation level of substation, and the insulation coordination between the insulation level of the protected electrical equipment and the arrester of the protection device is the most basic and important insulation coordination in the power system. In this paper, the working principle, characteristics and lightning current simulation of zinc oxide arrester are described. Then, taking a 420kV gis substation as an object, an electromagnetic transient simulation platform is built by using electromagnetic transient program (EMTP-ATP), which is used to simulate 420kV When the 400 kV side of GIS Substation is struck out of the line tower by lightning, the insulator flashover occurs, and then it invades the substation along the transmission line to generate Lightning Overvoltage on the equipment. Considering the operation mode of single line single bus single transformer with the most serious overvoltage, the overvoltage waveform of the voltage transformer at the outlet end and the main transformer port of the 420kV GIS Substation is obtained under the given arrester configuration scheme. Through calculation and analysis, the design meets the requirements of specifications and engineering practice.

In recent years, the digital transformation of power grid has become a new trend in the development of power grid enterprises. As a key link, the digitalization and intellectualization of substations have been included in the development plans of major power grid companies. This paper introduces a construction scheme of smart substation. By deploying edge Internet of Things agents, the construction scheme from on-line monitoring of electrical quantities of electric power equipment to data edge aggregation and to data application is realized, which provides an effective solution for the construction of smart substation.

The stator and mover of linear switched reluctance motors (LSRM) are double-salient pole structures, and their electromagnetic characteristics are highly nonlinear. There is a certain difficulty to control its speed. In order to realize the speed control of LSRM, a speed control strategy based on current chopper in low speed region of linear switched reluctance motor is proposed and designed. In this paper, the Matlab/Simulink simulation software is used to create the simulation model of the speed control system, and the simulation is carried out to verify the effectiveness of the control strategy.

The evaluation and management of the annulus pressure in the injection-production well of the gas storage is an important part of the wellbore integrity of the injection-production well. The Suqiao gas storage has experienced seven complete injection and production cycles. The production method puts high demands on the integrity of the injection-production well. At present, the injection and production wells of Suqiao Gas Storage in North China have different degrees of pressure in the oil casing annulus, which seriously threatens the safety of the wellbore of the gas storage. This paper analyzes the annulus pressure situation through different injection-production stages and different working conditions of gas storage injection and production wells, and establishes the environment by combining APIRP-2, NORSOK, ISO/TS 16530-2, SY/T5724—2008 and other standards. An annulus pressure evaluation process and an annulus pressure risk evaluation system that combines static and dynamic conditions. A four-level risk control model for gas storage injection and production wells is proposed. Annulus pressure well production management methods have been formulated to continuously improve gas storage. The operating environment and the treatment of injection-production wells with annulus pressure exceeding the maximum allowable value, especially the annulus blow-by problem of Well Su-20K-P1. The annulus pressure treatment has been carried out to achieve effective plugging and ensure the injection and production. The wellbore integrity has achieved a good repair effect, which has certain guiding significance for the evaluation of the wellbore integrity of the gas storage injection and production wells and the treatment of annulus with pressure.

Aiming at the problem that the stator excitation doubly salient synchronous motor (SEDSSM) has poor dynamic performance when using traditional vector control strategy, the observer model is used instead of the actual model for model predictive control in this paper. The discrete prediction model of the SEDSSM, including disturbance, is propesed, and the Luenberger disturbance observer observes the disturbance. Thus, the Luenberger observer based finite-control-set model predictive controller (LO-FCS-MPC controller) is constructed. The current are restrained and controlled directly by the cost function. The experimental results show that the control strategy proposed in this paper has good compatibility with the research object of this paper—the stator excitation doubly salient synchronous motor. The decoupling of the d and q axis is better achieved, and the system's dynamic performance is improved.

Combining with the current construction of Chinese electric power market, this paper puts forward the system structure model of virtual power plant based on balance control unit, then designs the customization construction mechanism of virtual power plant based on transaction, and realizes the customization of virtual power plant with internal resources aggregation according to the external contract requirements. Finally, the internal and external game mechanism of virtual power plant is analyzed. Through the master-slave game process between VPPO and BCUs, the local trading price is constantly updated, and the game equilibrium is finally reached, and the equilibrium between the overall benefit of VPPO and the benefit of BCU is realized.

The eddy current loss and shielding placement of traction transformer is one of the stable design links of the transformer. This paper takes a traction transformer as an object, and first analyzes and calculates the magnitude and distribution of the leakage magnetic field of the transformer. In order to reduce the eddy current loss of the tank, a magnetic shielding is arranged on the surface of the tank. To obtain the best shielding effect, the magnetic shielding structure of the front and rear sides of the tank is optimized from five aspects: width, height, thickness, distance between the shieldings and the distance between the shieldings and the tank. Based on the calculation results, the shielding characteristics of different shielding modes and structures of the tank are summarized.

Battery energy storage system (BESS) has been rapidly developed and widely used in power systems at home and abroad. However, the mechanism of BESS affecting short-circuit current is not well understood. The existing energy storage models are difficult to accurately reflect the dynamic characteristics during the fault crossing period. This paper researched the energy storage equipment modeling method which is suitable for short-circuit current analysis. And the simulation modeling method of energy storage battery body, DC/DC converter, VSC converter and its control system was studied. Aiming at the unbalanced operation state of energy storage AC side during asymmetric fault, a negative sequence component suppression method and model realization method in phase-locked loop (PLL) and inner/outer loop controllor were proposed. And a simulation analysis model suitable for short-circuit current contribution characteristics of energy storage equipment under different types of AC faults was constructed. The validity and applicability of the constructed energy storage model under the AC asymmetric short-circuit fault were verified by PSCAD/EMTDC simulation platform.

For the characteristics of the flexible DC line with fast rising fault current and difficult to open and close, An effective choice of protection scheme is ultra-high speed traveling wave protection. Analysis and simulation of fault traveling wave is an important prerequisite to determine the setting of various traveling wave protections. In this paper, based on the actual system parameters of the ±400kV Rudong offshore wind power flexible DC transmission project, a simulation model is built in PSCAD to imitate normal conditions and fault, and the fault traveling waves in the simulation results are analyzd.

Three-phase LCL-type grid-connected inverter is an important interface element of centralized photovoltaic (PV) gridconnected system. However, the inherent resonance in the LCL filter may lead to system instability. This research suggests an improved capacitor current feedback active damping (CCFAD) approach to address this issue. Firstly, the sequence impedance model of the PV grid-connected inverter is established using harmonic linearization method, and the resonant characteristics of the PV grid-connected system is revealed based on stability criterion. Subsequently, based on the proportional CCFAD strategy, an improved CCFAD strategy with delay compensation is proposed, which not only widens the positive damping zone of the traditional CCFAD, but also effectively improves the phase margin of the output impedance of the PV grid-connected inverter. Finally, the effectiveness of the proposed active damping control strategy is verified by simulation results.

In this paper, the finite element software SAP2000 is used to analyze the seismic response of the UHV captive voltage transformer under the action of earthquake, and the damping design of the structure is carried out. The numerical simulation results show that the transformer equipment can pass the assessment of 0.2g of 8 degrees of protection. With the damping design, the instrument transformer can pass the test of 0.4g with 9 degrees of protection. Under the same seismic action, the maximum stress response of the instrument transformer is reduced by 58.34%. The damping design makes the seismic ability of the transformer equipment increase by one degree, and the displacement response of the transformer equipment does not increase significantly.

Due to the lack of effective analysis of the short-circuit impedance curve in the main transformer winding deformation detection, the detection results are poor. Therefore, a method for developing a main transformer winding deformation detector based on swept frequency impedance method is proposed. Firstly, by combining power electronics and pulse power technology, a novel nanosecond pulse generator capable of outputting high voltage and stable repetition rate is proposed. Optoelectronic signals are selected as transmission signals to improve response speed and anti-interference ability, and the hardware circuit of the instrument is designed and optimized; Then, the frequency sweep impedance method is used to obtain and analyze the frequency sweep impedance curve and short-circuit impedance value of the transformer, reducing the detection error. The winding deformation detection is completed by comparing the changes before and after the transformer has a short-circuit fault. The results show that the standard deviation of the sweep impedance value of the main transformer winding deformation detector developed using this method is small and the detection accuracy is high.

Aiming at the rapid location of IGBT open circuit fault diagnosis, this paper builds a new single 13-level inverter topology in Matlab/Simulink based on the 13-level inverter topology, The FFT harmonic analysis tool is used to obtain the harmonic decomposition diagram of the output voltage under the open circuit fault of the IGBT tube under the reverse stacked modulation strategy. The amplitude and phase of each harmonic in the fault state are recorded, and the characteristic subharmonics and their characteristic vectors that can be used for fault diagnosis are determined through data analysis. The results show that the amplitude and phase of the second harmonic, third harmonic, fourth harmonic and second hundredth harmonic of the output voltage under FFT analysis can be used as input characteristic quantities for fault location.

Li-ion battery is widely applied in transportation and energy storage systems, due to their advantages of long lifespan, high-energy and high-power density. However, fast and healthy charging is critical when the Li-ion battery is applied in transportation due to the user experience and economics. Research has shown that pulse charging is an effective method for achieving both healthy and fast charging. However, existing research lacks the theoretically analysis on the pulse charging frequency affection to the pulse charging, which is critical for practical frequency setting for the Li-ion. To address this issue, this paper theoretically analyses the relationship between polarization voltage and pulse charging frequency, which is the core parameter that affect the fast and healthy charging, based on a dual polarization equivalent circuit model. DP model of a practical 35Ah ternary Li-ion battery is built in MATLAB and pulse charging is carried out. The simulation results are good fitted with the analysis on the polarization voltage and pulse frequency in the paper.

Distribution transformer is an important equipment in distribution network. The running state of distribution transformer directly affects the quality, safety and stability of power supply. In order to improve the prediction speed of distribution transformer operation reliability and ensure the stable operation of substation equipment, a distribution transformer operation reliability prediction model based on big data technology is constructed. Calculate the reliability characteristic quantity of transformer according to the predicted demand. Setting logistics forecasting nodes based on power dispatching data results. Combined with big data technology, the orthogonal prediction matrix is constructed, and the prediction model is constructed by using big data redundancy processing method. The test results show that the response time is less than 1.3s, the prediction accuracy is 96.37%, and the model is stable. It is proved that the proposed method can effectively improve the reliability prediction effect of transformer and has practical application value.

Due to its topology characteristics, VIENNA rectifier has the problems of neutral point voltage balance and current zero crossing distortion. This paper firstly introduces the existing HCB_ DPWM_I strategy, Based on this, the mechanism of current zero-crossing distortion of VIENNA rectifier is further analysed , and a new HCB_ DPWM_ II strategy is proposed, this modulation strategy can effectively solve the problem of current zero crossing distortion. Then the influence of these two modulation strategies on the midpoint voltage of the VIENNA rectifier topology is discussed, and the new HCB_ DPWM strategy is combined according to their different characteristics. The simulation and experiment prove that this modulation strategy can effectively solve the problems of neutral point voltage balance and current zero-crossing distortion existing in VIENNA rectifier.

D.C. potentiometer is widely used in precision measurement, verification, traceability and other fields due to its high accuracy and good stability. D.C. comparator potentiometer is by changing the automatic balance in the dc current comparator measuring the number of turns of the winding on the adjustment of measuring the resistance and determine a constant dc current, so as to obtain the continuous variable offset voltage constant resistance varying streaming potentiometer. According to JJF1059.1-2012Evaluation and expression of uncertainty in measurement, this paper analyzes the uncertainty of the measurement results which will useful to measurement uncertainty assessment of D.C. comparator potentiometers in future testing work.

With the increasing demand for power supply, it is particularly important to control the indoor temperature of substations in summer. In this paper, the temperature in the transformer room is studied: firstly, the transfer function of the transformer room ambient temperature is given, in which the influence of the transformer heat loss on the output variation is fully considered. Secondly, the model prediction method is used to control the input and output changes of the mathematical model. In order to achieve the control objectives, the constraint conditions of the variables are given in the control process. Finally, the experiment shows that the model predictive control method can control the output variation in a small range, which proves the effectiveness and accuracy of the model predictive control method for transformer room temperature.

The mandrel of the composite insulator is mainly made of mandrel and silicone rubber, and the mandrel material is Glass Fibre Reinforced Polymer (GFRP). If a defect occurs inside the mandrel of the composite insulator, it will eventually lead to brittle fracture, which will lead to extremely serious accidents. In this paper, the CST STUDIO SUITE tool is used to numerically model and analyse the internal defects of composite insulators. The frequency range is 26.5~40 GHz, and a simple method for judging composite insulators with different depths, sizes and types of defects is obtained: The second peak of the time-domain reflection coefficient of the conduction defect is the largest, the carbonization defect is between the no-defect condition and the conduction defect, and the air-gap defect is lower than the no-defect condition; and the shallower the air-gap defect depth, the larger the defect, The smaller the second peak value of the time domain reflection coefficient is, the larger the time domain signal area is. The simulation results show that it is feasible to use the microwave reflection method to detect the defects of composite insulators.

Due to operational requirements, high-voltage live working bypass cables are different from ordinary cables, and their magnetic field distribution is more complex than ordinary cables. The high-voltage cable is a three-core power cable. The sum of the three-phase currents transmitted by a three-core power cable during steady-state operation is 0. Traditional current transformers cannot measure the currents of each core wire, that is, each phase, and their measurement accuracy is difficult to ensure. This paper proposes an analytical calculation method for the surface magnetic field of a three core power cable based on the electromagnetic field equation that characterizes the electromagnetic characteristics of each part of the cable in a current flowing state. This method is not only suitable for symmetrical three-core cables, but also for other cables with arbitrary three-core distribution. Based on this analytical algorithm, by measuring the magnetic field on the surface of a three core power cable, it is possible to accurately measure the current of each core of the three core power cable.

In the process of unified data processing, due to the lack of analysis of its spatio-temporal attributes, the fitting effect of the data corresponding to the same household is poor. Therefore, a unified method for spatio-temporal data of power grid based on Beidou grid code is proposed. After the sphere corresponding to the coverage area of the grid is divided proportionally, the mutual conversion relationship between the coordinates of the grid spatiotemporal data and the longitude and latitude of the grid positioning corner is established, and the ontology grid spatiotemporal data in the grid grid module is divided into different classes. In the data unification stage, based on the control points (spatiotemporal data of the target power grid), the nonlinear interpolation method is used to realize the unification of multivariate data, and the spatiotemporal data set of the target power grid is interpolated to obtain the coverage surface of the power grid data, so as to realize the data unification on the basis of elastic interpolation. In the test results, the unified data fitting statistical parameters and Ljung Box Q (18) parameters under the design method are at a high level, with good fitting effect.

The main development target of the oil industry has shifted to high-temperature and high-pressure Wells. The buckling condition of the tubing string in the well has an important influence on the service safety of the tubing string. The existing calculation models do not consider the influence of casing string reducing diameter on the buckling state of casing string. Based on the temperature effect, gravity effect, casing contact and friction, casing string diameter reduction, the finite element model of casing string buckling in HTHP well X1 is established in this paper. The simulation results show that the string enters sinusoidal buckling state when the axial compression force is higher than 91kN, and spiral buckling state when the axial compression force is higher than 189kN. During the buckling process, the string safety factor is always higher than 1.25, and the string safety is supplemented. The research methods and results of this paper have a guiding role in the safe service of tubing string in HTHP Wells.

In recent years, lightning disasters have caused a large number of economic losses and casualties worldwide, posing a huge threat to human activities. How to effectively reduce the occurrence of lightning disasters has become a concern of researchers. Based on the original lightning data monitored by the lightning location system of Guangdong Province from 2010 to 2018, this paper analyzes the year-on-year change trend and distribution law of lightning to mine electric density in the the Pearl River Delta, and analyzes the change trend of probability distribution characteristics of lightning current amplitude in different years. Analyze meteorological data, analyze the annual change rules of the data, and visualize the data results. In the Inception convolutional neural network model established in this article, in the classification test set, the average probability of each sample being classified into each category under four rotational transformations is calculated, and the final score on the test set is 14.26446749. Compared with traditional ARIMA models, deep learning models can demonstrate good prediction performance. In particular, RNN-LSTM series models can be used as a conventional method for regional refined temperature prediction due to their strong series learning and prediction capabilities, providing guidance and suggestions for disaster prevention and mitigation work in power grids.

The large-scale access of new energy generation based on grid-connected inverters makes the grid have the characteristics of weak damping and low inertia, so the grid impedance is not negligible in most cases. As the power electronic interface between the new energy and the grid, the interaction between the inverter and the grid causes a series of oscillation problems. In this paper, the interaction between the voltage-source inverter (VSI) and the grid based on droop control is studied around the small-signal stability problem of the inverter after the new energy generation is connected to the weak grid. In this paper, the stability analysis of the VSI is carried out by establishing a state -space small-signal model, and the model is built in Matlab/simulink to verify the validity of the analysis results.

To address the problem that the output impedance of the bidirectional DC/DC converter is difficult to quantify due to the participation of multiple control links and the coupling of multiple parameters, a sensitivity analysis of the output impedance of the bidirectional DC/DC converter based on the Sobol method is proposed to quantify the influence of each parameter on the output impedance. Firstly, the mathematical model of the bi-directional DC/DC converter of the marine lithium battery energy storage system is established and the analytical expression of the output impedance of the bidirectional DC/DC converter is derived; secondly, the Sobol method is used to carry out the impedance sensitivity analysis and quantify the influence of each parameter. Finally, the output impedance model of the bi-directional DC/DC converter of the marine lithium battery energy storage system is measured by MATLAB/Simulink simulation, and the correctness of the sensitivity analysis is verified by changing the relevant parameters.

Thermonuclear fusion is the use of nuclear fusion reactions to produce energy. With higher energy density and less nuclear waste production than nuclear fission, thermonuclear fusion is considered to be a safer and more sustainable source of energy. However, the extreme conditions required to achieve thermonuclear fusion, such as high temperatures and densities, pose significant challenges to the design, construction and operation of fusion reactions. To overcome these challenges, iterative integrated design with training in thermonuclear fusion modelling and simulation has emerged as an important approach. With the continuous development of the crossover between artificial intelligence and physical design, combined with the powerful fitting capabilities of deep neural networks, deep learning was born, which utilises the highly anthropomorphic features of deep learning (DL) to learn itself through constant interaction and trial and error with the controlled object. Deep reinforcement learning has received great attention for its highly anthropomorphic features, pointto-point design ideas, and low a priori dependence. In this paper, deep learning is used to train and iterate on thermonuclear fusion models and their visualisation simulations to quickly obtain parameters for fusion reactions, significantly shortening the development cycle and providing more options and possibilities for the design and optimisation of fusion reactions, thus avoiding unnecessary costs and waste. The integrated design of thermonuclear fusion modelling and simulation training and iteration provides strong support for the research and application of thermonuclear fusion technology.

With the rapid development of integrated circuits, its electromagnetic compatibility becomes more and more prominent. This paper gives the most commonly used test standards and methods of electromagnetic emission, electromagnetic immunity and pulse immunity at home and abroad. In addition, from the aspects of test equipment, test conditions, test arrangement and test procedures, the TEM cell method and wideband TEM cell method of integrated circuit radiation emission measurement are introduced in detail. Suggestions on the use of two testing methods are given. The actual test results measured by this test method are analyzed. According to the actual test experience, this paper briefly introduces the differences between TEM cell method and wideband TEM cell method and stripline method, and gives the test points for attention.

Pressure gradient refers to the change in pressure per unit length in the direction of fluid flow. In oilfield production, the pressure gradient is directly related to seepage velocity and swept volume, the key targets of our analysis to ensure high and stable crude oil output. In nature, many solutions, such as extraction-based production, good control of the extraction difference, amplification of production pressure drawdown, water injection against the cap, fracturing, acidification, plug removal and profile control, are to adjust pressure gradient. In this paper, the relationship between pressure gradient and see page velocity and swept volume is investigated, points are drawn up for analysis in the investigation, and its application value is discussed in combination with oilfield development practice.

In 2021, wind power and photovoltaic power generation in China will account for about 11% of the total social electricity consumption, and it is planned to gradually increase the proportion of wind power and photovoltaic power generation in the future development and reduce the overall proportion of thermal power generation. China plans to increase the proportion of new energy by 2025, gradually achieve "carbon neutral", gradually increase the number of wind power installed in China, and adhere to the centralized cooperation with distributed development. In this paper, an improved four-unit two-zone system with a direct drive fan unit is built in the Simulink simulation environment. By establishing different fault modules, the dynamic characteristics and "virtual" power angle characteristics of the fan system under high voltage ride-through under different fault conditions are studied, and the transient stability of the system as a whole is studied by studying the "virtual" power angle of the phase-locked loop (PLL). The simulation results of the test system show that: (1) The longer the fault duration, the worse the transient stability of the system. Among the fault types, the single-phase ground fault fault depth is shallow, and the transient stability of the system is better. When the fault occurs at the midpoint of the line, the transient stability of the system is high. (2) With the gradual decrease of damping and bandwidth of the phaselocked loop, the transient stability of the system is smaller. (3) When different fault types occur in the grid-side converter, a high-voltage ride-through occurs after the low-voltage ride-through of the fault phase, while only a high-voltage ridethrough occurs in the non-fault phase.

By supposing the total apparent current fixed value, this paper proposes a novel approach for optimizing scheduling of a power grid, which does not depend on the exact model of a power grid. Based on the minimum energy consumption rule and each line's resister, the optimal method is proved. The method gives the minimum total energy consumption and the optimal scheduling current of each line. It has the following features: including liner and non-liner system, no needing system's mathematical model, high universality.

The current conventional line loss identification method mainly constructs the similarity matrix by calculating the similarity of line loss data, which leads to poor identification effect due to the lack of effective extraction of line loss data features. In this regard, the intelligent identification method of low-voltage grid segmentation under the analysis of line variation relationship of mapping consistency is proposed. The similarity of voltage time series mapping of low-voltage power grid is calculated to extract line loss feature data, and the EM algorithm is used to cluster and analyze the power operation data of line loss abnormal stations, and the vector features of line loss data under different Gaussian distribution states are calculated by constructing GMM model to realize line loss identification. In the experiments, the proposed method is verified for recognition accuracy. The experimental results show that the algorithm has a high recall rate and possesses a more desirable recognition accuracy when the proposed method is used for line loss data recognition.

In the photovoltaic grid-connected system, the various parameters of the photovoltaic cluster have been given by the manufacturer, but the control parameters of the photovoltaic inverter are unknown, which brings difficulties to the power system simulation. In order to solve this problem, a two-step parameter identification method based on the improved gray wolf algorithm is proposed, which expands on the traditional gray wolf algorithm, divides the wolf pack into encirclement group and catch-up group, and builds a model in MATLAB to verify the effectiveness of this method. The results show that the improved gray wolf algorithm has higher identification accuracy and stability in the identification of control parameters of photovoltaic inverters, which has guiding significance for the accurate modeling of power system and the stability analysis of power system.

mong many isolated DC-DC converter topologies, the dual active bridge (DAB) converter has become a popular research direction because of its symmetric structure, soft-switching characteristics, flexible control mode and other good performance. However, the overall optimization strategies for inductor current stress and soft-switching range still need to be improved. In addition, the traditional PI method is still inadequate in terms of dynamic performance. In this paper, full-bridge DAB converter is taken as the research object, and this paper proposes a minimum current stress control strategy based on triple-phase-shift (TPS) control using sliding mode control (SMC), in order to realizes the optimization of the traditional method.

Bidirectional LLC converters have good application scenarios because the advantages of LLC can be applied to bidirectional DC-DC converters. In this paper, the technical development and application scenarios of the bidirectional LLC converter are introduced and prospected. Firstly, the background of the LLC resonant circuit and bidirectional DC-DC converter is introduced, and then several main application scenarios of the bidirectional LLC converter and their characteristics are summarized. On this basis, the topologies of several main bidirectional LLC resonant circuits are introduced, and their control strategies are analyzed, and their respective advantages and disadvantages are discussed. Finally, the content mentioned in the paper is summarized and future development has prospected. With the development of new energy technologies, the large-scale application of bidirectional LLC converters should be expected.

The current technology for detecting thermal faults within transformers is relatively simple in its analysis of infrared thermal images, resulting in poorly correct detection results. To this end, an infrared detection technique for thermal faults inside the transformer based on higher order harmonic detection and independent component analysis is proposed. The thermal images of the internal transformer are acquired. Extraction of higher order harmonic distortion signal waveforms from thermal images. Transmission of the distortion signal waveform to obtain the minimum set of attributes for independent component analysis. Corresponding to the fault site, the detection of thermal faults inside the transformer is completed. Experiments have shown that the technique has a high correct rate of 99.57% on average for different types of thermal faults inside the transformer, which is of high application value.

The online detection of transformer capacity has important practical significance for the maintenance of the interests of electric power enterprises and the safe and stable operation of power grid. Due to the error of transformer operating environment, operating state or acquisition device, there is a lot of noise in the capacity detection data, which makes the on-line calculation of short-circuit impedance fluctuate and affects the capacity detection result. Based on the analysis of current data cleaning methods, a cyclic cleaning method based on BP neural network was proposed to clean historical data unsupervised and train the cleaning model synchronously. Through the trained cleaning model, online cleaning of detected data was realized. The simulation results show that the short-circuit impedance calculated by the online transformer capacity detection method based on the cyclic BP model tends to be stable, and the calculation error is maintained around 5%. Cyclic BP model is an unsupervised and intelligent data cleaning method, which can effectively improve the influence of noise data on detection results and improve the accuracy of transformer capacity detection.

As the penetration rate of distributed power sources in the distribution network gradually increases, this article proposes an improved interval coordinated voltage control strategy based on second-order cone programming theory to address the impact of system active power loss on voltage and voltage exceeding limits. The proposed regulation strategy is validated in the IEEE33 node model, demonstrating its effectiveness.

Aiming at the electromagnetic interference problem of UHV transmission lines on civil aviation communication, navigation and monitoring stations, the article carried out a 1000KV UHV on-site electromagnetic environment test, and then used the principle of space electromagnetic wave propagation to calculate the interference of 1000KV UHV transmission lines at the evaluation point based on the test results Signal field strength. Combined with the electromagnetic environment requirements of civil aviation communication, navigation and monitoring stations, the protection distance of 1000KV for the localizers involved in the example should not be less than 909.6m, and the protection distance for glide beacons should not be less than 350.9m.

The photovoltaic microgrid has complex characteristics such as multivariable and strong coupling. To solve the problem that traditional controllers are difficult to achieve ideal control effect, this paper introduces the improved second-order linear active disturbance rejection control technology to the control of the interface converter at the load end of the microgrid. Firstly, the plant is analyzed and the model information is extracted according to the state-space average method. Based on the traditional linear active disturbance rejection controller, the correction function and filter function are introduced to solve the problem of noise sensitivity measurement while the traditional linear active disturbance rejection control(TLADRC) is enhanced with disturbance rejection. On the premise of ensuring the stability of the system, the improved structure is used to improve the anti-disturbance and anti-noise of the system. Finally, the control strategy proposed in this paper and the voltage waveform of DC bus side under linear active disturbance rejection control are compared by simulation under different working conditions, which shows that the active disturbance rejection control strategy proposed in this paper has better control performance.

The relay protection equipment that has been put into operation is likely to fail due to incorrect or unreasonable setting values. Due to the strict operating system of the equipment, operation and maintenance personnel cannot directly conduct comprehensive testing and inspection of the relay protection equipment that has been put into operation. Simulate real relay protection input, output, and logic operation modules, and build a power grid model that is consistent with the external power grid environment of relay protection. Real time update the model of actual relay protection operation data is obtained through the secondary online monitoring system of the substation, and combined with data analysis algorithms, achieve normal operation, fault deduction, health status evaluation, and other services of relay protection in the digital twin environment, Thus, it effectively solves the potential safety hazards such as setting errors, hardware abnormalities, and circuit abnormalities in relay protection, which is of great significance for the safe and reliable operation of substations.

With the rapid growth of energy big data and types of sensors for energy data monitoring, a series of problems have been encountered in power data quality and data fusion. To solve these problems, we proposed a novel multi-source power data fusion method based on deep learning, in which the training network of energy big data is established. By adopting the idea of incremental learning and offline learning, the MCS-RF framework of energy big data is built in the online training of real-time image data, which can effectively mitigate the sparse problem of big data and encode the discrete data into one tailored for association rules. In this way, the redundant information in energy big data would be eliminated. We compared this new method with the traditional residual-based algorithms based on the power flow data in the SCADA system, and experimental results show that the proposed method can reduce the requirements for identifying energy data with bad noise and achieve higher accuracy.

In view of the ecological problems of electric bicycle batteries, this paper puts forward a low-cost and high-efficiency battery optimization device based on 51 single chip microcomputer, focusing on battery life, safety and environmental protection of discarded batteries. In terms of energy supply devices, the battery optimization device collects solar energy through solar panels and converts it into stable current for storage in the battery. As for the working device, a suitable sized heating patch is set as the heating device, a small temperature monitor is used as the monitoring device, and finally an anti heat alarm bell is used as the alarm device. In the aspect of intelligent constant temperature, the 51 single chip microcomputer is coded with keil 5. The system collects the temperature of the battery through the temperature sensor, and then converts it into a temperature signal and feeds it back to the single-chip microcomputer. If the measured temperature exceeds the set temperature threshold, it will issue instructions to the heating device to achieve the purpose and function. The battery optimization device is a device that combines 51 single chip microcomputer and heating plate to solve the problems of inundation of solar energy, poor battery endurance at low temperature and spontaneous combustion and explosion of batteries. It has the advantages of low cost and high efficiency.

An ultra-short-term wind power prediction model independent of meteorological data is proposed to solve the problem that there are some wild farms do not have meteorological sensors. The model consists of a two-stream network module and an attention module, one stream is used to extract spatial and time series features, the other stream is used to extract temporal features; the features of the two streams are fused with the attention mechanism to get final feature. Finally, the dimension of the final feature is reduced by the fully connection layers to get ultra-short-time wind power prediction. The validity and practicability of the prediction model are proved by an example analysis of a wind farm in east China for one year, the accuracy of prediction is high, which also provides a strong support for making power generation plan and power dispatching in actual scenarios.

The current transmission line intelligent inspection technology based on differential positioning uses UAVs to achieve inspection of transmission lines through carrier wave differential positioning technology, which leads to low inspection level accuracy due to low carrier wave phase differential accuracy. In this regard, the transmission line wide-area cluster intelligent inspection technology based on hybrid grouping network is proposed. The LTE hybrid network communication environment is constructed, the wireless network coverage is planned, and intelligent inspection information scheduling is carried out. The wireless network topology is controlled, and the cooperative inspection planning of robot and UAV is proposed. In the experiment, the proposed inspection technique is verified for inspection accuracy. The analysis of the experimental results shows that the intelligent inspection technology of transmission lines constructed by the proposed method has a high inspection accuracy.

With the increasing penetration of renewable energy in the distribution network, various internal and external factors will threaten the operation security of the power system. It is of great theoretical value to take appropriate measures to reduce the risk of power security in time for the early warning of the operation risk of the power system to ensure the stable operation of the power system. This paper proposes research on the early warning method of power security risk based on semantic analysis. According to that power system operation state and the early war index, the risk early warning information is obtained. The early warning information is analyzed based on a semantic analysis technology so that the aim of accurate early warning of the power safety risk is fulfilled. The experimental results show that the average recall rate of the proposed method is 12.29%, which verifies that the proposed method has good performance.

The advantages of switched reluctance motor and disk motor are pooled by axial field disk type reluctance motor. Therefore, the motor has the characteristics of low-cost, high-power density and compact structure. This paper has studied the fuzzy PI control to realize the control of axial field disk type switched reluctance motor, and gives the details of the control method. According to the contact between the motor’s switching angle and the output torque, fuzzy control rules are designed with motor speed and speed deviation rate, PI control is introduced to compensate the control blind area in fuzzy control at low speed. The simulation model of motor fuzzy PI control is established, and two simulation experiments of single PI control and fuzzy PI control are carried out respectively, the simulation results show that fuzzy PI control has good performance and is suitable for speed control of axial field disk type switched reluctance motor.

With the increasing size of power grids, the updating of grid mode data has become a time-consuming and tedious task in grid analysis and calculation. Because of this, this paper develops an auxiliary software for updating grid mode data based on the PSD-BPA power system analysis software, intending to improve the efficiency of updating equipment parameters and regulating power flow convergence. The data is input in the first step to identify the zone that needs to be updated; the second step sorts and processes the data's massive amount of information; the third reads the processed data and calculates the old and new loads; the generator output is then automatically adjusted to improve the power flow convergence of the mode data, and the fourth step updates the calculation results into the original file to obtain the new power flow data. The fourth step is to update the calculation results into the original file to obtain new power flow data and complete the update of the operation mode data. The application results show that it is remarkable in shortening the calculation time, improving the efficiency of system analysis and reducing the probability of errors.

The regulation ability of virtual power plants under different resource aggregation is different, which increases the difficulty of virtual power plants participating in the electricity market. Firstly, the characteristics of the internal resources of the virtual power plant are analyzed, and the output models of uncontrollable resources, semi-controlled resources and controllable resources are established respectively. Then, a virtual power plant adjustable space model is proposed, which can evaluate the adjustable capacity of virtual power plant under different resource aggregation. Finally, in order to verify the validity of the adjustable space model, the adjustable capacity of internal resources of the virtual power plant under the influence of TOU was evaluated, which effectively improved the flexible regulation ability of internal resources of the virtual power plant and supported the virtual power plant to participate in the electricity market trading.

Power equipment is the cornerstone of power system. Due to the advantages of infrared detection technology such as non-contact detection, power grid companies often use infrared detection technology to conduct regular inspection of power equipment. However, at present, the efficiency of manual inspection is low, and it is easy to be affected by the subjective experience and working time of inspection personnel, resulting in the misjudgment of the status of power equipment, which will be difficult to meet the requirements of real-time processing of infrared data of a large number of power equipment in the future. In addition, there are still many problems in the infrared image detection and recognition process of power equipment, such as low contrast of infrared image, mutual occlusion of power equipment and imbalance of positive and negative samples in the image. Based on the above difficulties, this paper develops the research of infrared image detection of power equipment based on deep convolutional neural network. This paper builds a power equipment infrared image detection network based on YOLOv4 algorithm in the Pytorch deep learning framework. Through the verification and effect testing of the proposed algorithm model, the results show that the proposed detection algorithm has realized real-time detection and achieved high detection accuracy. This lays a foundation for the realization of automatic and intelligent infrared image detection and recognition of power equipment, and lays a foundation for the subsequent state diagnosis of power equipment.

Power lines detection (PLD) plays a key role in ensuring the convenience and stability of social urban life. Deep learning-based methods have been widely investigated for power lines detection, which can prevent the operators from damage. However, due to the influence of light, angle, position and other factors, how to realize the reliable power line detection (PLD) automatically in real time and prevent accidents is a challenging problem. In this paper, a novel framework based yolcat is proposed to enhance the images and automatically detect power lines based on deep learning. The novel framework is by design fully convolutional, and it consists of two modules: (i) an image enhancement with Gaussian Pyramid, (ii) a line segment regressor. To evaluate the proposed methods, a public dataset is utilized to test the performance of line detection with standard metric of mean average precision (mAP). Results indicate that the proposed methods show the best performance of power line detection with the image enhancement across baseline methods

In order to ensure the privacy and security of user data transmission, when SM sends the user's power consumption characteristic data to the intelligent distribution transformer terminal, the distributed data model is used to aggregate the data. This paper proposes a study on improving the security of data information. The deep belief network (DBN) is introduced to compare the measured data with the expected data, better obtain the data characteristics, and reduce the dimension of the data, so as to reduce the calculation time of the algorithm and obtain the abnormal data faster; The intelligent distribution transformer terminal marks the SM of all consumers from 1 to N, and sends the execution data to the energy management system of the electric meter through the deep belief network to extract the characteristics. The faulty or damaged SM is checked and replaced, and more accurate NTL detection and analysis can be obtained. The proposed method can flexibly detect the data defects and anomalies of smart meters, and improve the practicability of energy / meter irregularity detection.

In order to solve the problems of complex engineering configuration and large workload of manual information verification between main and sub-stations during the construction of intelligent substations, we combine the plug-and-play theory in computer science with the intelligent technology of power system to realize the plug-and-play of smart grid. The interaction between the main sub-stations is realized through the sharing technology of SCD model for primary and secondary device mapping. The VF2 algorithm is utilized to obtain mapping relationships, and then to check the errors of manual diagrams, thus, automatic verification of manually maintained primary wiring diagrams is achieved. Through the application, the adopted technologies and solutions greatly reduce the complexity of manual work in substations and improve the construction efficiency of intelligent substations.

Considering the issue that the output torque performance and the noise, vibration and harshness (NVH) performance of permanent magnet synchronous motor (PMSM) are difficult to balance. In this paper, a structure with auxiliary slots for the stator and rotor is proposed, which weakens the radial electromagnetic force (REF) while maintaining the output torque, and reduces electromagnetic vibration and electromagnetic noise. Two dimensional electromagnetic models of the motor with different parameters of stator and rotor auxiliary slot are established respectively. According to the evaluation of REF density amplitude, radial air gap flux density, total harmonic distortion (THD) and average output torque, one group of auxiliary slot parameters is selected for combination. Compared to the outcomes of the original motor model, the noise value after improvement decreases by 20.2% and output torque basically unchanged. This shows that reasonable slotting parameters can improve NVH performance while maintaining output torque performance.