关键词: 局部放电, 去噪, 变分模态分解, 北方苍鹰算法

Abstract:

To address the issue of inadequate adaptivity of conventional denoising methods in partial discharge (PD) monitoring engineering practice, a Variational Mode Decomposition (VMD)-based adaptive denoising method was proposed. The approach incorporates an optimization module to determine the optimal number of intrinsic mode functions (IMFs) and penalty factor, followed by a Discrete Wavelet Transform (DWT) module. The objective function of the optimization model was proposed as minimizing the energy difference between the original signal and the reconstructed signal, which is the combination of IMFs that passed a kurtosis-based screening strategy using both hard and soft threshold methods. The Northern Goshawk Optimization (NGO) algorithm was applied for parameter optimization, successfully achieve the purpose of high adaptivity. The effectiveness of the proposed method was validated using both simulated data and field data from high-voltage tests. Simulated data comprised PD signals generated from all the four well accepted pulse waveform expressions superimposed with strong interference (white noise and 2 different narrowband interference) that fully submerged the PD signals. Processing with the proposed method increased the signal-to-noise ratio (SNR) from -18.46 dB to 12.54 dB. Field data were collected by a self-developed ultrasonic acquisition device in on-site high-voltage tests, exhibiting inherent interference from engineering environments and hardware circuitry. PD signals originated from a test sample engineered to simulate actual switchgear defect, thereby providing a more accurate representation of practical engineering scenarios compared to conventional standard PD models. Processing with the proposed method achieved an increasement of 38.19dB in SNR and a noise rejection ratio (NRR) of 9.88 dB, as well as PD waveform with clearly identifiable features highly consistent with the typical PD features of the defect sample. Comparative analyses were performed with DWT, EMD-DWT, and PSO-VMD-DWT algorithms, as the effectiveness and necessity of each module of the proposed method as well as the overall superiority were validated.

Key words: partial discharge, denoising, variational mode decomposition, northern goshawk optimization