Suzhou Electric Appliance Research Institute
期刊號： CN32-1800/TM| ISSN1007-3175

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SSA-VMD聯(lián)合改進小波閾值去噪算法在局部放電中應(yīng)用

來源：電工電氣發(fā)布時間：2025-04-03 12:03 瀏覽次數(shù)：3

SSA-VMD聯(lián)合改進小波閾值去噪算法在局部放電中應(yīng)用

孟小斐，劉紅兵
(太原科技大學 電子信息工程學院，山西 太原 030024)
 
    摘 要：針對電力設(shè)備局部放電信號的噪聲干擾問題，提出了一種麻雀搜索算法(SSA)、變分模態(tài)分解(VMD)與改進小波閾值去噪法相結(jié)合的去噪算法。以排列熵作為適應(yīng)度函數(shù)，使用麻雀搜索算法確定變分模態(tài)分解的模態(tài)數(shù)和懲罰因子并將含噪局放信號拆分成多個固有模態(tài)分量，再根據(jù)樣本熵確定有效閾值和去噪閾值。將樣本熵大于有效閾值的模態(tài)分量視為噪聲分量剔除，將樣本熵小于有效閾值且大于去噪閾值的模態(tài)分量進行改進小波閾值法處理，將去噪后的模態(tài)分量和小于去噪閾值的模態(tài)重構(gòu)完成信號去噪。在 MATLAB 軟件中進行對比仿真實驗，該算法在信噪比 xSNR 和均方根誤差 xRMSE 方面均有提升且保留了原始信號中的有效信息，驗證了其有效性。
    關(guān)鍵詞： 信號去噪；變分模態(tài)分解；麻雀搜索算法；局部放電；改進小波閾值法
    中圖分類號：TM744     文獻標識碼：A     文章編號：1007-3175(2025)03-0029-06
 
Application of SSA-VMD Combined Improved Wavelet Threshold
Denoising Algorithm in Partial Discharge
 
MENG Xiao-fei, LIU Hong-bing
（School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China）
 
    Abstract: To solve the noise interference problem of partial discharge signal of power equipment, an algorithm combining sparrow search algorithm (SSA), variational mode decomposition (VMD) and improved wavelet threshold denoising method is proposed. Firstly, with permutation entropy as fitness function, the sparrow search algorithm is used to determine the mode number and penalty factor of variational mode decomposition, and the signal with noise is divided into multiple inherent modal components. Consider modal components having a sample entropy higher than the effective threshold as noise components and eliminate them. For modal components where the sample entropy is lower than the effective threshold yet higher than the denoising threshold, carry out the treatment with the improved wavelet threshold approach. Subsequently,reconstruct the modal components that have been denoised and those with sample entropy below the denoising threshold, thus achieving signal denoising. In the MATLAB based comparative simulation experiments, the algorithm proposed in this paper demonstrates improvements in both the signal - to - noise ratio xSNR and root - mean - square error xRMSE. Moreover, it effectively preserves the valid information within the original signal, thus verifying its effectiveness.
    Key words: signal denoising; variational mode decomposition; sparrow search algorithm; partial discharge; improved wavelet threshold method
 
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