A review on prognosis and diagnosis of transformer oil quality using intelligent techniques based on dissolved gas analysis

The prognosis and diagnosis of evaluating the oil quality for high voltage electrical power transformers for maintenance is ever-demanding. The durability of transformers function is somewhat driven by the excellence of its insulation which degrades with time because of changes in temperature and content of moisture. The accurate diagnoses of faults in early stages and the efficient assessment of oil quality using an intelligent program is the key challenges for protection of transformers from incipient faults that occur during operation to avoid economic losses. The dissolved gases analysis in oil is a predictable approach in the fault diagnosis and evaluating the quality of insulating oil in transformers. In the recent applications of artificial intelligence include Fuzzy Inference, Neural Networks, Genetic Algorithm, State Space and Search, and other Expert Systems has the ability to meet DGA standards. This paper represents review of most of the methods used to diagnose faults and assessment of insulating oil for transformers through the dissolved gases analysis DGA. Further the future work provides alternate techniques for fault diagnosis and bio oil based insulations for transformer protection.