Partial Discharge Detection and Recognition in Random Matrix Theory Paradigm

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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