Recognition of impulse fault patterns in transformers using Kohonen's self-organizing feature map

Determination of exact nature and location of faults during impulse testing of transformers is of practical importance to the manufacturer as well as designers. The presently available diagnostic techniques more or less depend on expertized knowledge of the test personnel, and in many cases are not beyond ambiguity and controversy. This paper presents an artificial neural network (ANN) approach for detection and diagnosis of fault nature and fault location in oil-filled power transformers during impulse testing. This new approach relies on high discrimination power and excellent generalization ability of ANNs in a complex pattern classification problem, and overcomes the limitations of conventional expert or knowledge-based systems in this field. In the present work the "self-organizing feature map" (SOFM) algorithm with Kohonen's learning has been successfully applied to the problem with good diagnostic accuracy.