Energy Estimation of Partial Discharge Pulse Signals Based on Noise Parameters

Partial discharge (PD) detection has been proved as an effective tool for insulation condition monitoring of power equipment. The energy of PD pulses is valuable for studying the characteristics of PD activities. This paper proposes a method for estimating the energy of PD pulses in the presence of white noise and narrowband noise. First, a maximum likelihood (ML) estimator of the pulse energy is derived from the probability distribution of the energy spectral coefficients. To implement the ML method, the sampled data are divided into signal frames and noise frames. The noise frames are then utilized for extracting noise parameters using the 3F-C method. Eventually, these noise parameters are applied to the signal frames to find the ML estimate of the pulse energy. To verify the effectiveness of the proposed method, both simulated data and measured data have been processed using the proposed method and the conventional wavelet packet (WP) denoising method. Compared with the WP denoising method, the proposed method has a higher accuracy and is less susceptible to the lengths of the sampling time windows. The advantage of the method is more significant in unfavorable conditions where the signal-to-noise ratio is low and the accurate lengths of the PD pulses are difficult to determine.