Tan Jin

Fault diagnosis method of high voltage circuit breakers based on wavelet packet decomposition and ELM

In order to improve the method of the existing circuit breaker fault diagnosis, based on the existing shortcomings of mechanical fault diagnosis, a new method for fault diagnosis is proposed based on wavelet packet decomposition and ELM, and feasible diagnostic steps and analysis are also introduced. It uses wavelet packet decomposition extract characteristic vector of combines vibration and acoustic. Then, the characteristic vectors are used to build the input vector of ELM to conduct fault diagnosis. Experiments show that the proposed method is effective to diagnose the mechanical faults of high voltage circuit breakers.

Study of PSO-BP neural networks application in high-voltage circuit breakers mechanical fault diagnosis

High voltage circuit breakers is an important switchgear of the power system, and 80 percent of fault of high voltage circuit breakers is caused by mechanical failure. Considering a circuit breaker with VS1 type spring actuator as the subject, and the vibration signal under typical mechanical fault is collected. Then the wavelet packet and energy entropy are used to extract the characteristic value. A diagnosis method is proposed based on particle swarm optimization Hopfield neural network. This method to diagnosis fault mode for high voltage circuit breakers is established by analyzing vibration signals of the mechanism. The results show that the accuracy of the method to diagnosis fault mode based on PSO-BP neural network for high voltage circuit breakers is higher than the method of traditional BP neural network model, and the local minimum problem of traditional BP neural network model is prevented by using PSO-BP neural network model. The method of diagnosis fault based on PSO-BP neural network for high voltage circuit breakers is more accurate and feasible compared with traditional BP neural network.