Partial Discharge Measurement under an Oscillating Switching Impulse: A Potential Supplement to the Conventional Insulation Examination in the Field

Ren, Ming; Dong, Ming; Zhang, Chongxing; Zhou, Jierui

doi:10.3390/en9080623

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…The oscilloscope uses LeCroy-HDO6000A [11]. In order to simulate the PD defects in GIL, Figure 2 shows three aluminum typical defect models of the needle discharge defect, the floating discharge defect, and the free particle discharge defect designed in this study [12].…”

Section: Experimental Platform and Defect Modelmentioning

confidence: 99%

A Novel Partial Discharge Detection Method Based on the Photoelectric Fusion Pattern in GIL

Zang

Qian

Liu³

et al. 2019

Energies

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Optical detection and ultrahigh frequency (UHF) detection are two significant methods of partial discharge (PD) detection in the gas-insulated transmission lines (GIL), however, there is a phenomenon of signals loss when using two types of detections to monitor PD signals of different defects, such as needle defect and free particle defect. This makes the optical and UHF signals not correspond strictly to the actual PD signals, and therefore the characteristic information of optical PD patterns and UHF PD patterns is incomplete which reduces the accuracy of the pattern recognition. Therefore, an image fusion algorithm based on improved non-subsampled contourlet transform (NSCT) is proposed in this study. The optical pattern is fused with the UHF pattern to achieve the complementarity of the two detection methods, avoiding the PD signals loss of different defects. By constructing the experimental platform of optical-UHF integrated detection for GIL, phase-resolved partial discharge (PRPD) patterns of three defects were obtained. After that, the image fusion algorithm based on the local entropy and the phase congruency was used to produce the photoelectric fusion PD pattern. Before the pattern recognition, 28 characteristic parameters are extracted from the photoelectric fusion pattern, and then the dimension of the feature space is reduced to eight by the principal component analysis. Finally, three kinds of classifiers, including the linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN), are used for the pattern recognition. The results show that the recognition rate of all the photoelectric fusion pattern under different classifiers is higher than that of optical and UHF patterns, up to the maximum of 95%. Moreover, the photoelectric fusion pattern not only greatly improves the recognition rate of the needle defect and the free particle defect, but the recognition accuracy of the floating defect is also slightly improved.

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Section: Experimental Platform and Defect Modelmentioning

confidence: 99%