Diagnosing Failed Distribution Transformers Using Neural Networks

An artificial neural network (ANN) system was developed for failure diagnosis of distribution transformers. The diagnosis was based on the latest standards and expert experiences in this field. The ANN was trained utilizing the back propagation algorithm using, real (out of the field) data obtained from transformer failures of utility distribution networks. The ANN consists of six individual ANN according to six important factors used to give certain outputs. These factors are: the age of the transform, weather conditions, damaged bushings, damaged casing or enclosures, oil leakage, and faults in the windings. The six ANNs are combined in one ANN to give all the outputs of the individual six ANNs. The developed ANN can be used to give recommended complete diagnosis for working transformers to avoid possible failures depending on their operating conditions. Good diagnosis accuracy is obtained with the proposed approach applied and with the analysis of the attainable results.