System for recognizing partial discharge mode of power equipment

Abstract

The invention discloses a system for recognizing a partial discharge mode of power equipment, and the system comprises a signal preprocessing module which carries out the filtering and amplifying of asensor coupling signal. The signal processed by the signal preprocessing module is collected, and the data is transmitted to a data analysis module for analysis. A partial discharge PRPD graph and statistical features are calculated, and the statistical features of the partial discharge graph are inputted into a BP network neural algorithm input layer in a grouped manner, thereby obtaining different group of discharge results, and finally obtaining a discharge mode according to the different distributed weights. The system improves the recognition difficulty of the partial discharge mode. Thesystem can improve the accuracy and sensitivity through employing a high sampling rate and a high pulse capturing repetition rate.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of power equipment, in particular to a partial discharge pattern recognition system applied to power equipment. Background technique [0002] Partial discharge will occur in some weak parts of electrical equipment under the action of high field strength. Under certain conditions, partial discharge will lead to insulation degradation or even breakdown, endangering the safety of life and property. Therefore, live detection of partial discharge or Online monitoring can detect early defects or latent failures of equipment in time, which is of great significance to ensure the safety and stability of electrical equipment and power systems. There may be partial discharges caused by different types of defects inside the equipment, or the discharge signal is mixed with pulse interference, which requires pattern recognition of partial discharges to distinguish the degree of harm of different dischar...

AI Technical Summary

Problems solved by technology

[0005] (1) Statistical PRPD spectrum of partial discharge phase distribution is The spectrogram is triggered by a power frequency signal and continuously collected for 20ms. The spectrogram is calculated after multiple acquisitions. In the high sampling rate mode, the amount of collected data is large and the data analysis is slow

[0006] (2) In the phase analysis of the spectrogram, the traditional statistical features include skewness, steepness, peak number, etc. The statistical features have the same input weight in the partial discharge classifier, and there is no distinction between primary and secondary

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