SI Wen-rong, FU Chen-zhao, HUANG Hua, et al. Feature Extraction for Peak-time Series of Partial Discharge at HVDC[J]. High voltageapparatus, 2013, 49(11): 17-24.
SI Wen-rong, FU Chen-zhao, HUANG Hua, et al. Feature Extraction for Peak-time Series of Partial Discharge at HVDC[J]. High voltageapparatus, 2013, 49(11): 17-24. DOI: 10.13296/j.1001-1609.hva.2013.11.006.
Feature Extraction for Peak-time Series of Partial Discharge at HVDC
Three basic defect models of oil-paper insulation including corona
bounded cavity and surface discharge
and a typical discharge model of corona in air are described after the DC PD test and measurement system had been shown. The PD measurement system has a special anti-noise technique which resorts to the characteristic of pulse waveshape signals in time and frequency domain to achieve fast separation of random pulsed noise signals. Based on those works done above
the parameter Delta(t)is introduced for DCPD to form a new analysis approach
which perform the same function as the popular method named phase resolved partial discharge(PRPD)histograms of PD at AC voltage. Thirty-six fingerprints of DCPD are gained resorting to application of the standard statistic operators to some selected time and amplitude resolved partial discharge(TARPD)histograms. Then
feature selection are achieved by using a BP Neural Network optimized by genetic algorithm(GA-BP)
while the result is that only twenty optimized fingerprints are reserved.