Power distribution network fault diagnosis method based on random matrix and deep learning

Abstract

The invention discloses a power distribution network fault diagnosis method based on a random matrix and deep learning, and relates to the technical field of power distribution network fault diagnosis. According to the method, two basic tools of a random matrix theory and a deep learning technology are introduced to process a fault high-dimensional data set, wherein the random matrix theory has strict and flexible mathematical analysis capability in a high-dimensional space, and the deep learning technology has excellent high-dimensional data modeling capability; the high-dimensional characteristics of a fault are extracted by the random matrix theory and the deep learning technology; fault criterion is formed by adopting multi-feature fusion technology according to the extracted fault high-dimensional characteristics according to the process; the fault diagnosis model is constructed, effective fault diagnosis information can be obtained from the real-time data of the power distribution network through the fault diagnosis model, and fault real-time diagnosis is carried out according to the effective fault diagnosis information, so that the accuracy and the intelligent degree of thefault diagnosis of the power distribution network are improved.

Description

technical field [0001] The invention belongs to the technical field of distribution network fault diagnosis, and in particular relates to a distribution network fault diagnosis method based on random matrix and deep learning. Background technique [0002] Distribution network fault diagnosis aims to accurately and quickly detect, identify and locate faults in the distribution network, and is a key technology and breakthrough point to improve the reliability of distribution network power supply. The core difficulty of distribution network fault diagnosis is that the fault criterion is difficult to design. For example, in a small-current grounding system, the fault current characteristics of single-phase grounding are not obvious. The mainstream grounding line selection and fault location method in current engineering practice is still to select the grounding line by road test, and then manually inspect the line to find out. point of failure. Contents of the invention [0...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a distribution network fault diagnosis method based on random matrix and deep learning, thereby overcoming the shortcomings of the existing failure criteria for distribution network fault diagnosis that are not significant

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