A Fault Diagnosis Method Combined Fuzzy Logic with CMAC Neural Network for Power Transformers

Abstract

Dissolved gas analysis (DGA) is an effective method for early detection of incipient faults in power transformers. To improve the accuracy of fault diagnosis, a fault diagnosis method combined fuzzy logic with cerebellar model articulation controller (CMAC) neural network is proposed in this paper. The proposed fuzzy CMAC neural network (FCMAC) has an optimization mechanism to ensure high diagnosis accuracy for all general fault types. Firstly, it uses fuzzy logic to extract diagnosis rules from a lot of fault samples, and then, the extracted rules are employed to optimize CMAC network. Many real fault samples are analyzed by FCMAC for the purpose of verification, and the analyzed results are also compared with those analyzed by IEC ratio method and those by the CMAC neural network. The comparison results show that the proposed method has remarkable diagnosis accuracy.