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.
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.
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