Abstract—Transformers are a critical part of an electrical utility’s asset base. On-line monitoring and diagnostics is a useful tool to help operators to manage their assets and make decisions on continuing operation, maintenance or replacement. Dissolved Gas Analysis (DGA) is the heart of on-line monitoring as it is a well-established method of transformer diagnosis. DGA techniques are simple, inexpensive, and widely used to interpret gases dissolved due to the deterioration of the insulating oil of power transformers and hence to diagnosis, possibility of various type of faults in power transformer. Various diagnostic criteria based on gas analysis have been developed. In this paper, the application of many AI techniques have been presented such as Artificial Neural Network (AAN), Fuzzy Interface System (FIS), Genetic Algorithm (GA), Extended Relation Function (ERF), Bayesian Network (BN), Self Organizing Map (SOM) and Discrete Wavelet Network (WNs) Transforms, which can be used to increase the efficient and accurate diagnosis for off line and on line monitoring of power transformers.

Index Terms—Power Transformer, Dissolve Gas Analysis, Artificial Intelligence Techniques.

Naveen Kumar Sharma is with Electrical Engineering Department,National Institute of Technology, Hamirpur (H.P.), INDIA. (e-mail:naveen31.sharma@gmail.com).
Prashant Kumar Tiwari is with Electrical Engineering Department,National Institute of Technology, Hamirpur (H.P.), INDIA. (e-mail:prashant081.in@gmail.com).
Yog Raj Sood, Professor is with Electrical Engineering Department &Dean (Research & Development), National Institute of Technology,Hamirpur (H.P.), INDIA. (e-mail: yrsood@gmail.com).

Cite: Naveen Kumar Sharma,Prashant Kumar Tiwari and Yog Raj Sood, Member, IACSIT, "Review of Artificial Intelligence Techniques Application to Dissolved Gas Analysis on Power Transformer," International Journal of Computer and Electrical Engineering vol. 3, no. 4, pp. 577-582, 2011.