Study on Synthetic Diagnosis Method of Transformer Fault Using Multi-neural Network and Evidence Theory
Youyuan Wang · Proceedings of the CSEE · 2006
In the transformer fault diagnosis,the fault can be reflected by different characteristic signal from different side,due to complexity of fault reason and phenomenon of power transformer.Thus the synthetic disposal and cooperative analysis for multi-characteristic signal of transformer are needed.In this paper,a synthetic diagnosis method using multi-neural network and evidence theory for transformer fault diagnosis is presented,combining DGA data and routine electrical tests data,integrating two data fusion methods(ANN and evidence theory)by using their superiority and avoiding their disadvantages.The diagnostic results show accuracy and reliability based on multi-characteristic signal are improved effectively comparing with diagnosis based on single fault characteristic by using information from DGA data and routine electrical tests fully.