KEYWORDS: Meteorology, Power grids, Humidity, Temperature metrology, Data modeling, Education and training, Neural networks, Design and modelling, Unmanned aerial vehicles, Image processing
The purpose of this paper is to extract the effective information of power grid operation by using various meteorological monitoring and forecasting information. In all meteorological disasters, considering that the damage to power facilities caused by strong wind accounts for 68% of the total disasters, and considering that the snow and ice disasters in 2008 caused serious losses in large areas, this paper makes prediction for meteorological disasters and derivative disasters such as strong wind, ice, snow in a certain area, studies the probability of grid failure caused by ice and estimates the severity of snow to estimate the probability of grid failure caused by ice. Finally, a decision support system for comprehensive prevention of power grid faults is established. The research and application of this system can scientifically arrange the planning and operation of power grid dispatching, and effectively realize the disaster prevention and reduction of power grid facilities and the refined management of power grid safe operation.
With the increasing number of energy internet users year by year, manual inspections are gradually being replaced by unmanned inspections. The target case algorithm based on mixed convolutional neural analysis has been widely applied in grid related snow, rain, ice, and wind intelligent cases. However, in practical applications, it has been found that due to the small size of the collected targets to be located, the accuracy of the mixed convolutional neural analysis model will decrease when the shooting angle is tilted and the lighting conditions are poor. This is because the hybrid convolutional neural analysis of this algorithm is relatively low. When there is a significant difference in angle or illumination from the case, the positioning and collection ability of the model will be severely affected. On this basis, the feature fusion part of ONNX algorithm and the selection of loss function and feature vector frame size are improved, and the improved CNN fusion method is used to classify various data in grid related snow, rain, ice and wind. Actual measurements and repeated experiments have shown that this method can be effectively applied to the recognition of various grid related snow, rain, ice, and wind data, optimizing mixed convolutional neural analysis, and greatly improving the recognition efficiency of grid related snow, rain, ice, and wind data.
As the basic component of the power system, the prediction of power transmission line icing plays an important role in the power security protection. As a key factor affecting transmission line icing, cooling is widely introduced into the prediction model of power transmission line icing. Accurate understanding of the nonlinear characteristics between cooling and power transmission line icing has become the key to improving the level of transmission line icing prediction. At present, there is a lack of systematic and comprehensive analysis of the relationship between regional cooling and power transmission line icing in China. In this paper, a nonlinear analysis method of cooling and power transmission line icing is proposed, and a nonlinear analysis system is designed. In the specific analysis, the meteorological transmission line icing is first separated from the original transmission line icing, and then the maximum information coefficient is used to measure the linear or nonlinear relationship between different regions, different times, different cooling and meteorological transmission line icing. Finally, the efficiency and practicability of the proposed system are proved through the transmission line icing prediction experiment. The analysis results of the system can play a basic guiding role for the relevant research of transmission line icing prediction.
KEYWORDS: Analytical research, Data modeling, Data centers, Data storage, Machine learning, Data processing, Evolutionary algorithms, Data analysis, Internet, Databases
With the high-quality development of informatization, power grid enterprises promote the steady development of various businesses by virtue of strong technical barriers. In this paper, it is urgent to carry out research on the construction of intelligent data resource catalogue based on machine learning technology for the sake of consolidating the data application foundation of power grid enterprises, facilitating the potential value of data and optimizing the intelligent level of data services rapidly. Additionally, in this work, we analyze the framework, construction process and key technologies of intelligent data resource catalogue. Ultimately, this work realizes the intelligent construction of data resources in power grid enterprises.
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