Research on Intelligent Diagnosis Optimization Method of Power Transformer Winding Fault
Due to the different characteristics of different fault states in power transformer windings,it is difficult to ensure diagnostic effectiveness.Therefore,an intelligent diagnosis optimization method for power transformer winding faults is proposed.Taking the vibration signal of power transformer winding as the object,it is decomposed into a set of wavelet packet basis functions through scaling and translation,and the energy of each decomposed frequency band is used as the eigenvalue to form the eigenvector of power transformer winding fault signal.In the diagnostic stage,a kernel extreme learning machine was introduced to diagnose the fault state of the power transformer winding corresponding to the features.The results show that the effective diagnostic rate of this method for different types of winding fault states has reached over 85.0%,which has significant advantages compared to the control group.
power transformerwinding faultwavelet packet basis functionkernel extreme learning machine
万方数据
维普
