Adaptive fuzzy diagnosis system for dissolved gas analysis of power transformers

To enhance the fault diagnosis abilities for the dissolved gas analysis (DGA) of the power transformers, this paper proposes a novel adaptive fuzzy system for the incipient fault recognition through evolution enhanced design approach. Complying with the practical gas records and associated fault causes as much as possible, a fuzzy reasoning algorithm is presented to establish a preliminary fuzzy diagnosis system. In the system, an evolutionary optimization algorithm is further relied on to fine-tune the membership functions of the if-then inference rules. To make the diagnosis system intensively compact and the inference process more understandable, a pruning scheme is then developed to filter out the insignificant or redundant rules. The capabilities of the proposed diagnosis system for the transformer DGA decision support have been extensively verified through the practical test data collected from Taiwan Power Company (TPC).