Optimized Artificial Neural Network for the detection of incipient faults in power transformer

This paper presents optimized Artificial Neural Network to identify and detect incipient faults in power transformer. This study involved the development of Artificial Neural Network (ANN) models and embedding Evolutionary Programming (EP) as the computational technique to optimize the built ANN. The optimized ANN is namely as EPANN. As one of the most important equipment in electrical power system, the condition of the equipment need to be monitored closely to avoid any disturbances since its operating status directly influences reliability and stability of the overall power system. Historical industrial data of Dissolved Gas Analysis (DGA) were used and the analysis works are based on IEC 60599 (2007) standard. Based on the acquired findings, the EPANN is proven yields a very satisfactory result compared to non optimized ANN.