Research on hierarchical differentiation intelligent examination method for operational health of low voltage distribution network

Abstract

Abstract The operational health assessment of the Low-voltage Electricity District is the basic task of grid and refined management of low-voltage distribution network. Considering of the large number of stations and the lack of standardized management constraints, using the big data platform and multi-source heterogeneous data management technology, a kind of intelligent medical check-up method for the health of district station is proposed. Firstly, the self-organizing neural network clustering technology is used to classify the station area into 15 categories, which must take into account of geographical attributes, economic attributes and so on. Then, for each major type of area, the analytic hierarchy process is used to realize the subjective weight of the differentiated subject indicators. The entropy weight method is used to realize the objective weight of the three-level subdivision index, and the distance map method is used to realize the percentile mapping of the three-level subdivision index. Finally, the subjective and objective comprehensive weights are given to get the running health score of the station. Based on the actual platform data of a power supply unit in Shan Xi Province, the accuracy of the confusion matrix of the health classification of the station is about 92%, which verifies the effectiveness and applicability of the method.