The Transformer Fault Diagnosis Based on Quantum Neural Network

摘要：

It is very difficult to analyze the fault for dissolved gas analysis(DGA) because of the less and uncertain transformer gas information. In this paper, the Quantum Neural Nerwork(QNN) was applied to diagnosis the transformer fault by employing the DGA data. The QNN can be used to reduce the uncertainty of pattern recognition by allocating the uncertain data to the right fault pattern quickly and rationally as its model draws on the ideas of quantum phase computing and the complementary amendment relationship exists between the hidden layer output and phase shift parameters. The validity and feasibility of the proposed method were verified by testing the real DGA data. The results of the propoed model was compared with BP neural network in dealing with the actual DGA data also.

关键词： Quantum Neural Network Quantum Phase Phase Shift Parameter Fault Diagnosi

作者: Cai Guowei Liu Ning Yang Deyou

作者单位: School of Electrical Engineering Northeast Dianli University Jilin, China School of Electrical and Electronic Engineering North China Electric Power University Beijing, China

会议类型: 国际会议

会议名称: 2010 International Conference on Computer,Mechatronics,Control and Electronic Engineering(2010计算机、机电、控制与电子工程国际会议 CMCE 2010)

会议地点: 长春

会议语种:英文

页码: 396-400

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）