Distribution network electrical topology identification method and system based on edge calculation improved KNN

Abstract

The invention discloses a distribution network electrical topology identification method and system based on an edge calculation improved KNN, and relates to the technical field of topology identification in the field of distribution networks, and the method comprises the steps: S1, obtaining user voltage data, and recording the user voltage data as a data set A; S2, dividing the data set A into atraining set and a test set, performing mean value filling processing, and recording the data set A as a training set B and a test set C; s3, improving a KNN classification algorithm, taking a samplein the test set C to calculate with the training set B, and determining a phase to which the sample in the test set C belongs; S4, adding tested samples in the test set C into the training set B forupdating, and recording the samples as a training set D; s5, repeating the step S3, calculating the next sample in the test set C and the training set D, and determining the phase to which the next sample in the test set C belongs; and S6, repeating the training set updating in the step S4 and the classification calculation in the step S3 to obtain all phases to which the test set C belongs, thereby overcoming the problems of low topology identification efficiency and overlarge data processing pressure of the acquisition master station in the prior art.

Description

technical field [0001] The invention relates to the technical field of topology identification in distribution networks, in particular to a distribution network electrical topology identification method and system based on edge computing improved KNN. Background technique [0002] At present, there are three main methods of distribution network topology identification technology, that is, judging the corresponding station area and phase of low-voltage users: the first is to check the power outage and enter the household. This method is relatively traditional, requiring a lot of manpower, low efficiency, and slow speed. Manual input is required after manual verification, and direct and unified digital storage cannot be achieved, and the scope of use is limited. The second is to use wireless communication methods such as carrier waves for identification. By using different carrier types for identification, these methods require additional hardware facilities for communication ...

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Problems solved by technology

[0007] In view of this, the present invention provides a distribution network electrical topology identification method and system based on edge computing improved KNN, the purpose of which is to overcome the low efficiency of topology identification and the excessive pressure of collecting master station data processing existing in the prior art based on edge computing technology. big flaw

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