KEYWORDS: Power grids, Data modeling, Data processing, Quality systems, Power supplies, Standards development, Network architectures, Intelligence systems, Design rules, Analytical research
The demand for lean management of distribution network is growing. However, the current identification of distribution network data quality lacks a unified standard and cannot achieve automatic data verification and processing, which restricts the improvement of distribution network application level. In the actual distribution network application, more experts rely on their experience to solve the quality and availability problems of local distribution network topology data as needed, lacking a global data quality evaluation and solution. With the development of smart grid, the demand for lean management of distribution network is growing. However, the current identification of distribution network data quality lacks a unified standard and cannot achieve automatic data verification and processing, which restricts the improvement of distribution network application level. In actual distribution network applications, more experts rely on their experience to solve the quality and availability problems of local distribution network topology data as needed, Lack of overall data quality evaluation and solutions. This paper first combs the status quo and quality problems of distribution network topology data, collates and summarizes the current status quo of distribution network topology data, summarizes the problems existing in the application of distribution network topology data, and researches and classifies the problems, laying a foundation for the development of distribution network topology data quality evaluation system and distribution network topology data verification rules.
Under the development and reform of the new power grid, the technology of drawing single line diagrams also needs to be developed and reformed urgently. In the past, single line diagrams were drawn mostly by manpower. On the premise of given data and topology structure, the drafters first sorted out the topological relationship between the components, and then placed the components on the canvas with the principle of minimizing line crossings, and the canvas size needs to be reduced as much as possible and draw the graph W in a way that is easy to understand with the aid of CAD and other tools. This paper adopts a new decomposition model to solve the problem of power system single line mapping. First, the steps of electrical topology automatic mapping are decomposed into H steps. Using the knowledge of graph theory in mathematics instead of the form of polynomials, multiple processing objectives are processed separately, and H different methods are used to process them step by step. Then, the improved mapping algorithm is used to generate the plane embedding of electrical graph topology, which is used to achieve the goal of minimizing the intersection of lines. Then, the improved least cost flow graph method is used to process the graphic dexterity with the least cross. On the one hand, the graphic elements are evenly distributed, on the other hand, the horizontal and vertical orthogonal graphs are generated and the bending of the lines is minimized.
KEYWORDS: Data fusion, Fusion energy, Geographic information systems, Machine learning, Data storage, Classification systems, Power grids, Feature extraction, Network architectures, Information fusion
Due to the diversity of data types and data organization methods of electric multi-source heterogeneous data, the organization and storage requirements of heterogeneous data are also different, so the difference of heterogeneous data must be considered for data integration and fusion, the integration and fusion level of power distribution and distributed new energy data resources needs to be improved. The distribution network data resources including electrical equipment, spatial information, grid topology, power consumption information, operating conditions and other types of resources, taking into account the new energy, are obviously different in terms of quantity, scale, data model, data type, organization mode and other aspects. The data of distribution network and distributed energy comes from business systems in multiple professional fields, and there is a strong correlation between data resources at the business level. However, due to the certain independence between the data of various business systems for power distribution and the differences in field definitions and descriptions, traditional key field matching methods are difficult to achieve automatic data matching, and data fusion across business systems is faced with the problem of no uniform rules to follow.
KEYWORDS: Data analysis, Quality systems, Analytical research, Statistical analysis, Design and modelling, Data processing, Power grids, Data conversion, Mining, Data storage
The application of electric distribution in various fields of society is very common and indispensable. It has become one of the most important indicators to measure a country's comprehensive national strength. The level of electric service has a very important impact on the production and quality of life of the whole society. With the continuous expansion of the scope of electric index analysis, the traditional electric index analysis tools can no longer meet the needs of massive electric big data analysis, and the rise of big data technology provides an effective solution. This paper first introduces the definition, measurement indicators and common theoretical analysis methods of electric distribution data analysis. Based on the introduction of electric data analysis technology, it analyzes the development trend of electric quality analysis in the big data environment. Then, according to the characteristics of electric data of distribution network, an electric quality big data storage scheme based on Mongo DB is proposed, a power big data processing flow is designed, and a electric big data computing framework based on apache spark is built. On the basis of completing the construction of electric big data of distribution network, this paper expounds the overall design idea, system framework design and system function module design of electric data analysis system and gives the design scheme of electric quality analysis system based on B/S four tier architecture.
KEYWORDS: Data storage, Databases, Data modeling, Analytical research, Power grids, Computer security, Matrices, Detection and tracking algorithms, Fusion energy, Data fusion
The traceability and analysis of power distribution business data depend on the accurate description of the correlation network of power distribution data resources. In response to the above requirements, this paper first builds a multi-source heterogeneous power distribution and distributed new energy data flow topology network. Aiming at the complex relationship between power distribution and data resources and the flow characteristics of data, topology network construction technology is used to build bus type, star type, ring type, tree type, mesh and other topology networks according to the data association characteristics to achieve accurate description of the data association network. Then, based on the data association relationship network of power distribution and distributed energy, the data traceability must be calculated, and the traceability analysis can only be carried out if there is connectivity between the data. In response to the above requirements, the research on connectivity analysis algorithm based on power distribution and distributed new energy data network was carried out. By solving the connection path between the data nodes, the connectivity analysis requirements of the data nodes in the directed connected network and the directed connected network can be met. The data traceability of power distribution and distributed new energy is to trace the source of data in all business links, which plays an important role in the safe production, problem tracing, root cause analysis and other businesses of power distribution. In response to the above requirements, carry out research on tracing technology of allocated electricity and distributed new energy
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