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

被引:34
作者
De, A [1 ]
Chatterjee, N [1 ]
机构
[1] Jadavpur Univ, Dept Elect Engn, Kolkata 700032, W Bengal, India
关键词
artificial neural network (ANN); fault diagnosis; impulse testing; self-organizing feature map (SOFM); transformer;
D O I
10.1109/61.997923
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:489 / 494
页数:6
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