Partial Discharge Random Noise Removal Using Hankel Matrix-Based Fast Singular Value Decomposition

摘要

The most effective method for insulation assessment in an electrical power apparatus is partial discharge (PD) detection. During measurements, the interference from the background environment hampers the PD signal and reduces its measurement accuracy. This article discusses on the implementation of a Hankel matrix (HM)-based fast singular value decomposition (H-FSVD) technique for removing noise from the PD signals. The data are first represented into HM structure, with appropriate sampling then using the Lanczos process, the HM size is reduced and through singular spectral analysis thresholds are fixed for noise detection and removal. This algorithm has been examined on simulated as well as PD signals measured on two different laboratory environments from transformers with real and simulated noise. The experiment is part of a series of experiments to detect PD patterns related to realistic transformer defects. The denoising performance of H-FSVD is compared with the wavelet-based denoising methods, empirical mode decomposition method, and normal SVD. Numerical results show that H-FSVD efficiently removes the noise with less computation time, even for large size data.