Feature Eztraction and Pattern Recognition of Signals Radiated from Partial Discharge

摘要：

The artificial neural networks based BP algorithm is used to recognize two typical discharge patterns,corona and spark. In order to have a comparison,feature extraction based on waveform parameter and time-frequency analysis were used separately to provide the training input.The results show that the highest average recognition rate based on waveform parameter reaches 92.5％,while this based on time-frequency is 95％.On the contrary, the lowest average recognition rate based on waveform parameter is 70％,while this based on time-frequency is 90％.This indicates that time-frequency analysis is more effective and more suitable for discharge pattern recognition.

关键词： partial discharge BP network feature eztraction pattern recognition

作者: LIU Weidong LIU Shanghe HU Xiaofeng

作者单位: Electrostatic and Electromagnetic Protection Research Institute,Mechanical Engineering College,Post Code:050003,Shifiazhuang,Hebei,China

会议类型: 国际会议

会议名称: Asia-Pacific Conference on Environmental Electromagnetics CEEM2009(第五届亚太环境电磁学学术会议)

会议地点: 西安

会议语种:英文

页码: 114-117

在线出版日期: 2009-09-16（万方平台首次上网日期，不代表论文的发表时间）