Transformer condition analyzing expert system using fuzzy neural system

Power transformers being the major apparatus in a power system, thus the assessment of transformer operating condition and lifespan have obtained crucial significance in latest years. Dissolved gas analysis (DGA) is a sensitive and reliable technique for the detection of incipient fault condition within oil-immersed transformers, which provides the basis of diagnostic evaluation of equipment health. The first part of this paper deals with an expert system that utilizes fuzzy logic implementation into dissolved gas in oil analysis technique. To improve the diagnosis accuracy of the conventional dissolved gas analysis (DGA) approaches, this part proposes a fuzzy system development technique based combined with neural networks (fuzzy-neural technique) to identify the incipient faults of transformers. Using the IEEE/IEC and National Standard DGA criteria as references, a preliminary framework of the fuzzy diagnosis system. In the second part, artificial neural network (ANN) based fault diagnosis is presented, which overcomes the drawbacks of the previously applied fuzzy diagnostic system that is it cannot learn directly from the data samples. These expert system also consider other information of transformer such as type, voltage level, maintenance history, with or without tap changer etc. These proposed approaches provide the user a more accurate result and better condition awareness of the transformer.