Fault Diagnosis Method of HV Circuit Breaker Based on Wavelets Neural Network

Fault diagnosis of HV circuit break has been investigated extensively as an important device in the field of power system. In view of the shortcoming of the traditional neural network, such as the slow convergence rate and the local minimum easy to form, fault diagnosis method of HV circuit breaker is proposed to remedy the defects of traditional neural network based on wavelets neural. This method adopts wavelets function rather than the hidden nodes of traditional neural network, which is propitious to conducive to achieve a rapid convergence of online learning. This work firstly discussed the principles of fault diagnosis method in detail, and then compared diagnosis effect using wavelets function with that of the traditional neural network. The results show that the training speed and classification effect of wavelets neural network are superior obviously to those of traditional neural network. Wavelets neural network based on vibration signals is more suitable in application to the fault diagnosis of HV circuit breakers.