Partial Discharge Signal Denoising Method Based On Frequency Spectrum Clustering And Local Mean Decomposition

Suppressing the background noise of partial discharge (PD) is one of the key issues for accurately diagnosing the state of electrical equipment insulation. To solve this problem, this study proposes a new denoising method based on frequency spectrum clustering and local mean value decomposition. First, the K-means clustering is employed on the frequency spectrum to pick out narrow-band interference frequencies. Next, the PD signal with white noise is decomposed by local mean decomposition into different product function components, and the components contain more information about time-frequency than the intrinsic mode functions originated from empirical mode decomposition. Besides, the adaptive threshold is utilised to eliminate white noise in the components. Finally, the denoised PD signal is synthesised by these denoised components. The proposed method and three traditional methods are applied to simulated and field-detected noisy PD signals, respectively. The results of the evaluation indicators confirm that the proposed method is better than the existing PD denoising methods.