Partial discharge pattern recognition using random matrix theory

A new methodology of partial discharge (PD) pattern recognition under random matrix theory (RMT) paradigm is proposed in this paper. RMT has exhibited great potential values in the ongoing researches of big data in power systems applications. This paper extends RMT from Gaussian noise to non-Gaussian pattern by modeling time series as random matrix. Based on that, the characteristic parameters of PD signal are obtained from the eigenvalues distribution of assembled random matrix and corresponding covariance matrix. The BP neural network is used to perform the pattern recognition of six partial discharge models. The results show that the recognition rate of proposed method is above 85% and very robust to the pulse interference.