Power distribution network rapid topology identification method and system based on K nearest neighbor classification, and readable storage medium

Abstract

The invention discloses a power distribution network rapid topology identification method and a system based on K nearest neighbor classification, and a readable storage medium. According to the method, measurement data of node voltage of the power distribution network are used, a K nearest neighbor algorithm is adopted to classify all nodes according to spatial distances between the nodes, then only mutual information values between node pairs in each classification need to be calculated, and then a mutual information topology recognition algorithm is utilized to recover a topology structureof the power distribution network. Wherein the K nearest neighbor classification algorithm is adopted, and the k-nearest neighbor point of each node is obtained by calculating the spatial distance between the nodes in the power distribution network, so that the calculation amount of the mutual information values of the nodes in the topology identification process is reduced. The characteristic ofhigh accuracy of a topology identification algorithm based on mutual information is reserved, and the operation time of topology identification is shortened.

Description

technical field [0001] The invention belongs to the field of distribution network identification, and in particular relates to a fast topology identification method, system and readable storage medium of a distribution network based on K-nearest neighbor classification. Background technique [0002] In recent years, my country has paid more and more attention to the modernization of distribution network, and the development of distribution network has achieved remarkable results, but there is still a certain gap compared with the international advanced level. At the same time, the access of a large number of distributed power sources and electric vehicle charging piles has changed the structure and operating environment of the distribution network. In addition, frequent feeder upgrades, new power users, distribution network optimization and Reconfiguration will lead to an increasingly complex structure of the distribution network, and a significant increase in uncertainty an...

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Problems solved by technology

[0006] (1) The distribution network topology identification method based on Pearson correlation coefficient has low operating efficiency, is easily affected by the accuracy of voltage measurement data, the algorithm stability is poor, and the accuracy rate of topology identification is low

[0007] (2) In the distribution network topology identification method based on mutual information, it is necessary to calculate the mutual information value between all node pairs according to the voltage measurement value, the calculation amount is large, and the running time of topology identification is long

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