Abstract
Partial discharge (PD) detection has been widely applied to high voltage cable systems for several decades. In this paper, three kinds of insulation defects in XLPE cables are designed and tested at step-wise DC voltage. The PD developing progress of each defect cable is divided into two stages based on the severity degree of PDs. Based on the compressed sensing (CS) theory, a novel method used for recognizing PD patterns at DC voltage is proposed. Firstly, both the statistical features of PD repetition rate and the norm characteristics of time domain features are extracted to create a high-dimensional feature space. Then each test sample from the feature space is sparsely represented as linear combinations of training samples, and the sufficiently sparse one is obtained via 1-norm minimization. Finally, the PD pattern can be recognized by minimizing the residuals between the test sample and the recovered one. The experimental data is analyzed by the proposed method, and the results show that the patterns of both PD source and PD stage are recognized precisely, when the combination solution of features and the 1-norm minimization algorithm are determined appropriately.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Dielectrics and Electrical Insulation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
