Denoising of Partial Discharge Signal in Stator Using Wavelet Transform with Improved Thresholding Function
Dong Liang Yang, Kunlong Song, Ruijie Yi, Haonan Xiong, Xiaomei Yang · Applied Sciences · 2025
Partial discharge (PD) signals are used to evaluate the insulation condition of stators in electrical machines. Their measurements are often heavily corrupted by ambient noise, making denoising essential for effective detection and analysis of PD signals. Wavelet thresholding techniques are widely applied to denoise PD signals. However, existing hard and soft thresholding functions introduce oscillation or deviation into PD signals after wavelet reconstruction, particularly under high-noise conditions. This paper proposes an improved thresholding function for the wavelet threshold denoising method that effectively overcomes the oscillation issue associated with the hard thresholding function and the constant deviation of the soft thresholding function. Additionally, wavelet basis selection based on the correlation coefficient and an adaptive threshold value is integrated with the improved thresholding function to implement the wavelet threshold denoising method. The proposed technique is applied to both simulated and real-world measured PD signals to evaluate its performance across different signal-to-noise ratio (SNR) levels. Compared with traditional soft and hard thresholding functions, simulation results confirm the superiority of the improved thresholding function, especially under high-noise conditions. At an input Gaussian noise level of −10 dB, the proposed method yielded an SNR that was 1.20 dB and 2.66 dB higher than those of the hard and soft thresholding functions, respectively.