Estimating the Vital Parameters in Transformer Oil Using Soft Computing Technique

Abstract

Power transformers are the costliest equipment in power system. Transformer may get failed by the failure of insulation system. Monitoring the transformer is essential to keeping continuity in power distribution. Goal of presented work is to predict the transformer oil critical parameters with low cost for monitoring purpose of transformer. In this project one of the soft computing technique, artificial neural network have been constructed to predict different critical transformer oil parameters. The prediction is performed through modeling the relationship between the predictable parameters and critical parameters. The process of predicting these oil parameters statuses is carried out using various configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning Algorithm was implemented. Subsequently, a cascade of these neural networks was deemed to be more promising according to the correlation between the parameters.