Application of GA-BP Neural Network on Partial Discharge of Pattern Recognition in GIS

摘要：

In order to improve pattern recognition based on partial discharge detected with ultrasonic method, repetitiveness of partial discharge(PD) in different defects are measured and 34 steady defect characteristic parameters are got from the extracted 43 characteristic parameters. Then, the 24 effective characteristic parameters are filtered as input of neural network. Finally, an improved GA-BP neural network is proposed. After training, the result shows that the application of GA-BP neural network improves the recognition rate.

作者: HU Quan-wei ZHANG Liang WU Lei LI Jun-hao LI Yan-ming CHEN Jun LIU Wen-hao LU Jun CHEN Min

作者单位: School of Electrical Engineering, Xian Jiaotong University, Xian 710049, China Zhengzhou Power Supply Company ,Zhengzhou 450006,China Hubei Electrical Power Testing & Research Institute, Wuhan 430077, China

会议类型: 国际会议

会议名称: 2011 1st International Conference on Electric Power Equipment-Switching Technology(第一届电力设备开断技术国际会议 ICEPE)

会议地点: 西安

会议语种:英文

页码: 513-516

在线出版日期: 2011-10-23（万方平台首次上网日期，不代表论文的发表时间）