Power equipment discharge signal separation and classification method based on kernel principal component analysis

Abstract

The invention discloses a power equipment discharge signal separation and classification method based on kernel principal component analysis. The method comprises the steps of: (1) obtaining a power equipment discharge signal characteristic quantity as a sample data set; (2) processing the sample data set by using kernel principal component analysis to obtain a principal component variable as a new characteristic quantity; (3) clustering the discharge signals through a K-means clustering method to obtain type tags of different types of discharge signals; (4) selecting a principal component variable which can best reflect different discharge signal differences, and drawing a two-dimensional scatter diagram and a three-dimensional scatter diagram; and (5) carrying out gate circling among different types of discharge signals according to the type tags to finish automatic classification of the discharge signals. According to the method, principal component characteristics are extracted byadopting a nonlinear method, an appropriate kernel function is selected to extract the principal component of the discharge signal from the original discharge pulse signal, and automatic separation and classification of multiple discharge source pulses and multiple interference source pulses are realized, so that discharge type identification of power equipment is realized.

Description

technical field [0001] The invention relates to the field of separation and classification of discharge signals of electric power equipment, in particular to a method for separation and classification of discharge signals of electric power equipment based on nuclear principal component analysis. Background technique [0002] Insulation faults of high-voltage power equipment are often caused by penetrating discharges inside or on the surface of insulating materials, and penetrating discharge channels are generally developed from tiny discharge signals. The weak discharge current pulse at the initial stage of discharge is also far lower than the stray pulses in the power system, and is easily overwhelmed by background noise or corona interference signals. [0003] Based on the characteristics of the discharge signal of power equipment, if the discharge signal cannot be separated from various background spurious signals, it is impossible to identify and classify the fault type ...

AI Technical Summary

Problems solved by technology

Conventional discharge signal separation and classification methods are usually based on the pulse phase distribution spectrum (PRPD) and pulse waveform statistical characteristic parameters to identify a single discharge type, but the actual field operation of power equipment is affected by the harsh operating environment on site, and there are multiple discharges at the same time. The possibility of discharge types, the traditional separation and classification methods cannot identify multiple discharge types and multiple interference signal sources

Images