Partial Discharge Denoising of High-Voltage Cables for High-Speed Trains Based on Singular Spectrum Analysis and ICEEMDAN Decomposition

Partial discharge (PD) detection is an effective method to evaluate the insulation status of cables for high-speed trains. Various factors in the detection process due to external interference, which make it difficult to get the ideal partial discharge signal. Periodic narrowband interference and random white noise are the main interference factors in the partial discharge signal. A partial discharge denoising method based on singular spectrum analysis and improved adaptive noise-complete ensemble empirical modal decomposition (ICEEMDAN) is proposed in this paper. With the method, By selecting the maximum value of singularity slope as the dividing point between narrowband interference and valid signal after grouping, the singular spectrum analysis algorithm selects the valid signal part for signal reconstruction, the noisy PD signal with white noise interference is decomposed into multiple eigenmodes by ICEEMDAN decomposition, which effectively avoids the modal blending in the empirical modal decomposition, sorts the IMF components further by the kurtosis criterion, and reconstructs the filtered IMF components, which extracts the pure ideal PD signal. Through denoising analysis of simulated PD signals and measured signals, the Noise Rejection Ratio of this method is 19.1836 in the laboratory and 16.389 in the depot, both higher than other methods. Therefore, this method has better denoising effect on noisy PD signals and can retain the original partial discharge information to a higher degree.