Study on a spatially selective noise filtration technique for suppressing noises in partial discharge on-line monitoring

Partial discharge (PD) on-line monitoring has been used extensively to evaluate the insulation condition of electrical equipment in service. However, its sensitivity and credibility are affected by strong noise such as communication signals and random noise produced by the detection system itself. In recent years, the wavelet transform has been used for signal processing. In this paper, a novel method for extracting PD signals from noise is presented based on the nonstationary algorithm. The proposed algorithm combines multiresolution analysis with hard threshold in order to construct a wavelet transform-based stationary-nonstationary (WTST-NST) filter. The simulating signals and some experimental data obtained in a power plant are processed by using the new filtration technique. The results indicate that this method can extract the PD signal from noise effectively. Compared with other methods adopted to extract PD signals, the WTST-NST filter performs more accurately, and with lower computation time. It has been used in PD on-line monitoring systems successfully.