Classification Feature Extraction Algorithm for GIS Partial Discharge Pulses
Bao Yongsheng1, Hao Fengjie2, Xu Jianzhong3, Zhang Yuanhang1
1.State Grid Ningxia Electric Power Company Yinchuan Branch Yinchuan 750001 China 2.State Grid Tianjin Electric Power Maintenance Company Tianjin 300232 China 3.State Grid Ningxia Electric Power Maintenance Company Ningxia Yinchuan 750001 China
Abstract The partial discharge (PD) pulse group classification spectrum constructed based on traditional PD time-frequency information can only provide low-dimensional feature characteristics of PD pluses.When the classification algorithm requires more characteristics of PD pluses to complete the classification work,the abovementioned methods do not work well.This article presents an equivalent time-frequency entropy algorithm to extract the multidimensional characteristics which present the PD pluses waveform feather,and then constructs the PD pluses groups equivalent time-frequency entropy classification spectrum.The spectrum is further combined with the improved fuzzy C means clustering algorithm to complete the classification work of different types of PD pluses groups.The testing results based on gas insulated switches (GIS) prove the validity and rationality of this algorithm,which provides both experimental and theoretical basis for the development of PD online monitoring and identification system based on single artificial defect model.
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2016, Vol. 31 
