Noise Reduction Method for Partial Discharge Fluorescence Fiber Sensors Based on Optimized Empirical Wavelet Transform

Abstract

A novel self-adaptive denoising method utilizing optimized empirical wavelet transform (EWT) is proposed to enhance the sensitivity of partial discharge (PD) fluorescence fiber sensors. The optimized EWT enhances the spectrum segmentation capability of conventional EWT via spectral kurtosis (SK). The SK at the optimal window length of noisy PD fluorescence signal is calculated to determine compact support of the Fourier spectrum for subsequent signal decomposition. Frequency components with SK value over the statistic threshold are used to rebuild the PD fluorescence signal. Subsequently, residual noise in the reconstructed signal is removed through adaptive wavelet threshold denoising. To evaluate the performance of the proposed method in denoising numerically simulated and experimentally obtained noisy PD fluorescence signals, outcomes are compared to those of the novel adaptive ensemble empirical mode decomposition (NAEEMD) method, EWT method, EWT joint with kurtogram (KEWT) method, and correlation spectral negentropy (CSNE)-based method. Quantitative metrics and running time are used to assess denoising performance and execution efficiency, respectively. Simulated and experimental results demonstrate that the proposed method possesses a superior noise reduction effect compared to the other four methods while restoring the detail of the PD fluorescence signal flooded by serious noise and consuming reduced computational cost.