Wavelet-based partial discharge denoising using hidden Markov model

Wavelet-domain hidden Markov models (HMMs) have recently been introduced and applied to signal and image processing. The advantage of the method is that the HMMs measure the dependency between the wavelet coefficients and have no free parameters in denoising. In this paper, the HMMs method is applied in reducing partial discharge (PD) white noise. The effectiveness of the method is demonstrated by using numerical simulations and real-world data of neutral point current of generator. Compared with the shrinkage method, the result shows that the HMMs method is better in enhancing signal-to-noise ratio and reserves more PD impulses.