A defect recognition method and a system of a partial discharge signal

Abstract

The invention discloses a defect identification method of partial discharge signal, which comprises the following steps: (1) acquiring training samples of partial discharge signals representing several partial discharge insulation defect types, extracting phase-resolved pulse sequence data based on the training samples, and normalizing the phase-resolved pulse sequence data; (2) the sparse self-encoder model being trained by the normalized phase-resolved pulse sequence data, and the dimension-reduced training eigenvectors being obtained based on the trained sparse self-encoder model; 3) training the extreme learning machine model by using the training eigenvector; (4) the PD signal to be identified being input into the trained sparse self-encoder model to extract the test feature vector, and the test feature vector being input into the trained limit learning machine model to obtain the defect types represented by the PD signal to be identified. The invention also discloses a defect identification system of a partial discharge signal.

Description

technical field [0001] The invention relates to an identification method, in particular to an identification method for defect identification. Background technique [0002] High-voltage equipment causes partial discharge due to internal defects, which causes faults and poses a threat to the stable operation of the power system. Therefore, the research on partial discharge has great significance to ensure the safety of power system. [0003] With the development of artificial intelligence, neural network, support vector machine (Support Vector Machine, SVM), Bayesian network, decision tree, etc. are widely used in fault diagnosis of high-voltage equipment, especially for the identification of partial discharge patterns. [0004] However, the current pattern recognition methods still have many deficiencies. For example, when the number of samples is small, some pattern recognition methods are unstable and the recognition rate is not high. Normal sample size), some pattern re...

AI Technical Summary

Problems solved by technology

[0004] However, the current pattern recognition methods still have many deficiencies. For example, when the number of samples is small, some pattern recognition methods are unstable and the recognition rate is not high. Normal sample size), some pattern recognition methods will have the problem of shifting the classification interface

In addition, the current pattern recognition method is slow in recognition speed and cannot meet the actual needs, which makes the existing pattern recognition method subject to great restrictions in the actual application process.

Images