高压电器2018，Vol.54Issue(8)：207-211,5.DOI:10.13296/j.1001-1609.hva.2018.08.032
多神经网络与证据理论的变压器故障诊断方法
Fault Diagnosis Method of Transformer Based on Multi-neural Network and Evidence Theory
张文元 1赵卫国 2晋涛 2俞华 2王伟2
作者信息
- 1. 山西国际能源集团有限公司,太原 030022
- 2. 国网山西省电力公司电力科学研究院,太原 030001
- 折叠
摘要
Abstract
In order to solve the problem that it is difficult to locate and identify power transformer faults quickly and accurately, a synthetic diagnosis method using multi-neural network and evidence theory for transformer fault diagnosis is presented. This method combines the strong nonlinear fitting ability of neural networks with the advantages of evidence theory in dealing with subjective and objective contradictions in uncertain reasoning problems. Various kinds of dissolved gas data in oil and conventional electrical test data are dealt by using neural network's excellent abilities of learning, memory and recognition. Integrating data fusion methods, neural network's preliminary results are diagnosed by evidence theory and the accurate diagnosis of transformer faults is realized. It has been shown by experiments that the accuracy rate of transformer fault diagnosis is up to 73％.关键词
变压器/多神经网络/证据理论/故障诊断Key words
transformer/multi-neural network/evidence theory/fault diagnosis引用本文复制引用
张文元,赵卫国,晋涛,俞华,王伟..多神经网络与证据理论的变压器故障诊断方法[J].高压电器,2018,54(8):207-211,5.
