An Intelligent Fault Diagnosis Method for CNN-SVM Circuit Breaker Based on Quantum Particle Swarm Optimization

Abstract

In order to make use of fewer fault data samples to diagnose the main fault types of circuit breakers accurately in real time, an intelligent fault diagnosis method for circuit breakers based on convolutional neural network (CNN) and quantum particle swarm optimization (QPSO) is proposed. Firstly, the key features of the circuit breaker operational signal are extracted through the CNN model, and the extracted feature vectors are input into the support vector machine (SVM) for fault diagnosis. In order to improve the diagnostic performance, this paper uses QPSO algorithm to optimize the parameters of the classifier, it effectively solves the local optimal problem. The experimental results show that the method presented in this paper has achieved good results in fault diagnosis of circuit breakers, and the accuracy of diagnosis is up to 100%, which highlights the superiority of this method.