DGA based insulation diagnosis of power transformer via ANN

An improved Back Propagation (BP) artificial neural network is utilized to assess the insulation condition of a large oil immersed electric power transformer in this paper. After a complete comparison of performances between a few different network architectures, a new kind of BP network structure with a promoted learning algorithm is chosen to train the diagnostic network. Furthermore, some techniques in the reliability analysis of data is introduced into the BP network so as to realize pre-treatment of the data acquired through Dissolved Gas Analysis (DGA), as it is a useful tool for assessing oil-paper insulation. It is verified by the DGA data from substations, that the improved BP algorithm bound with the technique of data pre-treating obtained much higher accuracy. So, it is worthy of being applied for insulation diagnosis in utilities.