Open AccessPartial Discharge Detection and Recognition in Random Matrix Theory Paradigm
Author(s) -
Lingen Luo,
Bei Han,
Jingde Chen,
Gehao Sheng,
Xiuchen Jiang
Publication year - 2017
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2016.2634622
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The detection and recognition of partial discharge (PD) is an important topic in insulation tests and diagnoses. Take advantage of the affluent results from random matrix theory (RMT), such as eigenvalue analysis, M-P law, the ring law, and so on, a novel methodology in RMT paradigm is proposed for fast PD pulse detection in this paper. Furthermore, a scheme of time series modeling as random matrix is also proposed to extend RMT for applications with non-Gaussian noise context. Based on that, the eigenvalue distribution property is used for PD pattern recognition, which is completely new compared with traditional phase resolved PD and time-resolved PD methods. The simulation and experimental results show that the proposed methods are efficient, reliable, and feasible for PD detection and recognition especially for online applications.
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