An Advanced Partial Discharge Recognition Strategy of Power Cable. (13th September 2015)

• Record Type: Journal Article
• Authors: Bi, Xiaotian; Ren, Ang; Li, Simeng; Han, Mingming; Li, Qingquan
• Other Names: Sahalos John N. Academic Editor.
• Is Part Of: Journal of electrical and computer engineering. Volume 2015(2015)
• Publication Date: 2015-09-13
• Subjects: Computer engineering -- Periodicals; Electrical engineering -- Periodicals; 621.3905
• DOI: 10.1155/2015/174538
• Languages: English
• ISSNs: 2090-0147

Abstract

Abstract : Detection and localization of partial discharge are very important in condition monitoring of power cables, so it is necessary to build an accurate recognizer to recognize the discharge types. In this paper, firstly, a power cable model based on FDTD simulation is built to get the typical discharge signals as training samples. Secondly, because the extraction of discharge signal features is crucial, fractal characteristics of the training samples are extracted and inputted into the recognizer. To make the results more accurate, multi-SVM recognizer made up of six Support Vector Machines (SVM) is proposed in this paper. The result of the multi-SVM recognizer is determined by the vote of the six SVM. Finally, the BP neural networks and ELM are compared with multi-SVM. The accuracy comparison shows that the multi-SVM recognizer has the best accuracy and stability, and it can recognize the discharge type efficiently.