Application of neuro-fuzzy scheme to investigate the winding insulation paper deterioration in oil-immersed power transformer

In this paper, an attempt has been made to examine the effectiveness of Neuro-Fuzzy Scheme (NFS), to identify the deterioration of the winding insulation paper (WIP) in a oil-immerged power transformer, and to compare its performance over conventional methods (IEEE/IEC). The comparison of convergence characteristics of IEEE and IEC approach reveal that the NFS approach is quite faster in investigations leading to reduction in computational burden and give rise to minimal computer resource utilization. Simultaneous identification of deterioration of the WIP and operating conditions in oil-immersed power transformer has never been attempted in the past using NFS. The technique proposed in this paper provides not only best dynamic response for the deterioration of the WIP diagnosis and condition assessment of power transformer but also present its appropriate maintenance scenario as well. This approach will address a proactive assertion to the power utilities for effective realization of electrical health of oil-immersed power transformer under consideration. In this paper, testing analysis of 25 transformer samples has been carried out to demonstrates the robustness of the investigated four status conditions (Normal Operation - NO; Modest Concern - MCI; Major Concern - MCMI and Imminent Risk Failure - IRF) for wide changes in operating condition and loading condition perturbation.