An Adaptive Neuro Fuzzy Inference System for fault detection in transformers by analyzing dissolved gases

Safe operation of elements of power systems plays a crucial role in maintaining the reliability and safety of the system. Transformers being a key element in power systems need to be maintained and monitored on a regular basis. Dissolved gas analysis has been used as a reliable tool in maintaining the safe operation of transformers for a long time. Analysis of dissolved gases is analytical and often interpreted differently by different users and methods. The scope of Artificial Intelligence tools in dissolved gas analysis has become critical with increasing number of transformers being used in power systems coupled with rapid expansion of transmission and distribution components. In this work we have designed an analysis system based on different Artificial Intelligence methods like Neural Networks, Fuzzy, and Adaptive Neuro-Fuzzy for analyzing dissolved gas and give interpretation about possible faults. Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling technique has emerged as one of the soft computing modeling technique for power transformer. The objective of this paper is to design an ANFIS model for dissolved gas analysis of power transformers. The prediction ability of the ANFIS is also tested using limited data set for model training.