REVIEW ON POWER TRANSFORMER INTERNAL FAULT DIAGNOSIS

Abstract

Power Transformers have emerged as an integrated part of a power system. Any fault in the transformer can cause a severe outage, which therefore necessitates continuous monitoring and diagnostics of its operation. The faults in Windings, OLTC, Core, Terminals and Fluid are 88% of the total faults in the Power Transformer. The renewed thrust in smart power system networks along with the development of advanced methods in the monitoring and diagnostics has resulted in major impetus to research in the related domain. Artificial neural network (ANN) is powerful tool for the problem with small sampling and high dimension. ANN is applied to establish the power transformers faults classification. The experimental data from Tamil Nadu State Transmission Utility is used to illustrate the performance of proposed ANN models which in turn gives the performance of windings, Core, OLTC and oil. RBF classifier is trained with the training samples. Finally, the normal state and the fault types of transformers are identified by the trained classifier. The test results indicate that the ANN approach can significantly improve the diagnosis accuracies for internal power transformer fault classification.