Method and its application of partial discharge rating based on multidimension feature extration

LIU Yukuan, MA Lixin, ZHANG Jianyu, HUANG Yanglong

(Department of Electrical Engineering School of OpticalElectrical and Computer Engineering,
 University of Shanghai for Science & Techuology, Shanghai 200093, China)

Abstract: Aiming at the problems of difficulty to accurately quantify the classification for partial discharge status, the new method of PSOSVM classification was investigated.By this method,multiple feature spaces were mapped to different SVM kernel functions, each kernel function and penalty parameters were optimized via particle swarm optimization(PSO). A ultraviolet sensing electrical inspection system carried the new method was presented. Combined with range finder and ultraviolet sensor of the system,four kinds of feature data were obtained and returned to the terminal PC. Those data were composed of ultraviolet light spot area,ultraviolet pulse waveform, measured distance and angle.In this way,the model took full advantage of sensitivity of UV signal. According to the classification model set up by the test data, this system can be used to diagnose and rating abnormal discharge of equipment. The results indicate that the new method can complete the abnormal discharge rating accurately according to the data back,and the classification model of PSOSVM can prevent the blindness of selecting parameters and also has significantly higher accuracy than the traditional SVM.

Key words: partial discharge; UVdetector; high voltage routing inspection; support vector machine for particle swarm optimization (PSOSVM)