Fault Diagnostic Mothed Using Wavelet Neural Network for Power Transformers

Building a fault diagnostic model is the key point of condition-based maintenance(CBM) technology for the power transformer.In order to enhance the fault diagnostic ability of conventional dissolved gas analysis(DGA) in power transformer,this paper proposes an incipient fault diagnostic method using wavelet neural network(WNN).The discrete affine wavelet function is employed as an activation function of the hidden layers,and the learning ratio and the momentum coefficient were used to train the feed-forward BP algorithm.The weight value and bias value of WNN were adjusted by this method,so the computation quantity of WNN was reduced,the convergence speed of WNN was increased,and learning capability and fault diagnosis accuracy of WNN were improved.Experimental results based on 500 actual gas records of civil power transformers on the same condition demonstrate that the WNN model provides the highest diagnostic efficiency in faults pattern cognition which can reached over 87% compared with conventional ratio methods(under 65%) and BP neural network(81%),while WNN requires less convergence time and shows better stability than BP neural network.So the method proposed can satisfy the needs of CBM technology.This paper also draws a conclusion that choosing the proper hidden layer node numbers is another key point to fault diagnosis based WNN and ANN,in addition,the fault diagnostic efficiency will be improved with the increase of hidden layer nodes,however the efficiency will slightly be lowered and reach saturation when the number of hidden layer nodes reaches a certain level.