High-voltage circuit breaker fault diagnosis method based on GWO-KFCM

Abstract

The invention discloses a high-voltage circuit breaker fault diagnosis method based on GWO-KFCM. The method comprises the following steps of: 1, carrying out the band-pass filtering of a sound signal,and constructing a voiceprint feature through generalized S transformation; 2, carrying out variational mode decomposition on a vibration signal to obtain a permutation entropy; 3, combining the voiceprint features and the permutation entropy features to construct a feature vector set; and 4, pre-classifying the feature vector set through GWO-KFCM to obtain an optimal feature vector sub-set, inputting optimal feature vector sub-set data into a model SVM, obtaining a final diagnosis result through classification, extracting voiceprint and permutation entropy features respectively by utilizingthe joint complementary advantages of the sound and vibration signals, then performing clustering optimization by adopting GWO-KFCM to obtain an optimal clustering center, and finally, adopting an SVMfor fault diagnosis. The mechanical fault of the high-voltage circuit breaker can be quickly and effectively detected, and the reliability of fault diagnosis of the high-voltage circuit breaker is improved.

Description

technical field [0001] The invention relates to the technical field of high-voltage circuit breaker fault diagnosis, in particular to a GWO-KFCM-based high-voltage circuit breaker fault diagnosis method. Background technique [0002] A high-voltage circuit breaker is a switching device that can turn on or off a high-voltage circuit under normal or fault conditions. It plays a dual role in control and protection in the distribution network system. Its operating status directly determines the performance of the entire power system. Therefore, it is of great significance to carry out fault diagnosis on high voltage circuit breakers. At present, the maintenance of high-voltage circuit breakers relies on a regular outage maintenance system. This method has high maintenance costs, and is prone to failure during maintenance. It is blind, not targeted, time-consuming and laborious, and may cause large-scale power outages. The fault types of high-voltage circuit breakers are varied,...

AI Technical Summary

Problems solved by technology

At present, the maintenance of high-voltage circuit breakers relies on a regular outage maintenance system. This method has high maintenance costs and is prone to failure during maintenance. It is blind, not targeted, time-consuming and labor-intensive, and may cause large-scale power outages.

The fault types of high-voltage circuit breakers are varied, and there are complex and diverse nonlinear relationships between fault phenomena and fault causes. Traditional fault diagnosis methods are not effective. With the rapid development of artificial intelligence and computer technology, a number of high-voltage circuit breakers have Intelligent diagnosis methods, such as error backpropagation (BP) neural network-based high-voltage circuit breaker fault diagnosis method, high-voltage circuit breaker fault diagnosis method based on radial basis (RBF) neural network, etc., but these methods have slow convergence rate and easy Falling into local poles, complex selection of extended parameters, etc., based on the existing problems in the current process of mechanical fault diagnosis of high-voltage circuit breakers, it is urgent to propose a new fault diagnosis method for high-voltage circuit breakers

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