An Improved PSO-Based Fuzzy Ensemble Classifier for Transformer Fault Diagnosis

摘要：

To deal with the flaws of single neural network in process of transformer insulation fault diagnosis, for example, low diagnosis precision, long training time and bad generalized ability etc, in the paper we propose an ensemble fuzzy neural classifier based on improved particle swarm optimization (IPSO). The method fully utilizes the advantages of particle swarm optimization such as fast seeking speed and easy realization mode etc, the integrated time of the overall system therefore become very short. Thus, more neural networks are applied to diagnose transformer faults at the same time, and the final conclusion is identified based on all achieved results. Hence, the integrated neural network possesses higher diagnosis precision and reliability, and is an ideal pattern classifier. In the end, a practical application in transformer insulation fault diagnosis indicates that the proposed method is very effective.

关键词： PSO Fuzzy ensemble classifier Transformer Fault diagnosis Entropy.

作者: Hongsheng Su Feng Zhao

作者单位: Lanzhou Jiaotong University, Lanzhou 730070, P.R. China

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 589-594

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）