Extension neural network for power transformer incipient fault diagnosis

Abstract

An extension neural network (ENN)-based diagnosis system for power transformer incipient fault detection is presented. The ENN proposed is a combination of extension theory and a neural network. Using an innovative extension distance instead of Euclidean distance (ED) to measure the similarity between tested data and the cluster centre, it can effect supervised learning and achieve shorter learning times than traditional neural networks. Moreover, the ENN has the advantage of height accuracy and error tolerance. Thus, the incipient faults of power transformers can be diagnosed quickly and accurately. To demonstrate the effectiveness of the proposed method, 40 sets of field DGA data from power transformers in Australia, China, and Taiwan have been tested. The test results confirm that the proposed method has given promising results.