Fault Identification in Transformers through a Fuzzy Discrete Event System Approach

A new fault detection and identification (FDI) scheme for transformer faults are suggested in this paper. The new method is based on fuzzy discrete event, from now FDES, composed from between a transformer´s measured outputs and its faults. In the FDES, events and state membership functions take values between zero and one. All events occur at the same time with different membership degrees. The main advantage of the suggested scheme is that different types of incipient or abrupt faults of transformers can correctly be identified. Principal component analysis (PCA) is mainly used for fuzzy event generation purpose. Event based FDES diagnoser involves fuzzy IF-THEN rules created by an artificial neural network (ANN) based on radial basis functions to identify incipient faults in transformers. It shows single or multiple faults and occurring degrees of these faults. The study is concluded by giving some examples about distinguishability of the single or multiple fault types in transformers. The real-time laboratory experiments verify the effectiveness of the suggested method.