Power Transformer Fault Diagnosis Based on Integrated of Rough Set Theory and Neural Network

摘要：

In this paper, a rough set (RS) and neural network (NN) integrated algorithm based fault agnosis for power transformers, using dissolved gas analysis (DGA) is proposed. This approach takes advantage of the knowledge reduction ability of rough set and good classified diagnosis ability of NN.Power transformer fault parameters are reduced by rough sets, then work as BP neural networks input vector. Neural network initial weights are set according to the confidence of reduction parameters. Simulation results show that the combination of rough sets with neural network has good diagnostic ability.

关键词： Rough Set Neural Network Power Transformer Fault Diagnosis Attribute Reduction

作者: ZHOU Ai-hua SONG hong XIAO hui ZENG Xiao-hui

作者单位: Institute of Automation and Electronic Information Sichuan University of Science & EngineeringZigong Institute of Automation and Electronic Information Sichuan University of Science & Engineering Zigon

会议类型: 国际会议

会议名称: 2012 International Conference on Intelligent System Design and Engineering Applications(2012年智能系统设计与工程应用国际会议 ISDEA 2012)

会议地点: 三亚

会议语种:英文

页码: 1463-1465

在线出版日期: 2012-01-06（万方平台首次上网日期，不代表论文的发表时间）