Feature Extraction and Fault Identification of Partial Discharge in Multi-signal Transformer
Partial discharge pattern recognition has been established as a standard diagnostic tool for monitoring the operation of electrical equipment.Intelligent state recognition is the development trend of transformer state recognition,but the existing intelligent state recognition has the disadvantages of single model and low recognition accuracy.In order to overcome this shortcoming,a multi-dimensional information source transformer partial discharge fault identification method based on D-S evidence theory was proposed.Firstly,wavelet packet decomposition was used to extract the energy features of high frequency partial discharge signal and ultrasonic signal.Then,according to the selected feature set,the CNN(convolutional neural networks)model and CNN-SVM(convolutional neural networks-support vector machine)model were established respectively.Finally,the D-S(dempster-shafer)evidence theory was used to effectively integrate the output results of the two signal recognition models.The results show that using the proposed wavelet packet decomposition energy feature set as input vector,the recognition rates of the two signals CNN-SVM models reach 95％and 81.67％,which are 3.33％and 8.34％higher than CNN respectively.The overall performance of D-S evidence theory fusion method is better than that of CNN and CNN-SVM,and the accuracy and consistency are improved by 3.33％and 16.66％respectively.The effectiveness and feasibility of this method are verified.
万方数据
维普
