Fault diagnosis of power transformer using SVM and FCM

In this study, we are concerned with fault diagnosis of power transformer. The objective is to explore the use of some advanced techniques such as SVM and FCM and quantify their effectiveness when dealing with dissolved gases extracted from power transformers. The proposed fault diagnosis system consists of data acquisition, fault/normal diagnosis, identification of fault and analysis of aging degree parts. In data acquisition part, concentrated gases are extracted from transformer for data gas analysis. In fault/normal diagnosis part, SVM is performed to separate normal state from fault types. The determination of fault type is executed by multi-class SVM in identification part. Although the inputted data is normal state, the analysis of aging degree is performed by considering the distance measure calculated by comparing with reference model constructed by FCM and input data. Our approach makes it possible to measure the possibility and degree of aging in normal transformer as well as the identification of faults in abnormal transformer. As the simulation results to verify the effectiveness, the proposed method showed more improved classification results than conventional methods.