一種基于小波變換和全變差的局部放電信號組合去噪法
戴宇1,2,王錄亮1,3,楊旭4,5,張靜4,周思遠(yuǎn)5,潘子君5,姚雨杭5
(1 海南電網(wǎng)有限責(zé)任公司電力科學(xué)研究院,海南 ???570311;2 東北電力大學(xué) 建筑工程學(xué)院,吉林 吉林 132012;
3 海南省電網(wǎng)理化分析重點(diǎn)實(shí)驗(yàn)室,海南 ???570311;4 國網(wǎng)電力科學(xué)研究院武漢南瑞有限責(zé)任公司,湖北 武漢 430074;
5 武漢大學(xué) 電氣與自動化學(xué)院,湖北 武漢 430072)
摘 要:現(xiàn)場測量所得到的局部放電(Partial Discharge,PD)信號會被白噪聲污染,有必要對其進(jìn)行去噪處理?;谛〔ㄗ儞Q閾值去噪和全變差去噪方法,提出一種小波閾值和全變差組合去噪算法。該算法將兩種方法進(jìn)行融合,吸收了它們各自優(yōu)點(diǎn),有效減小PD信號由于小波閾值去噪而造成的波動誤差,并避免了全變差去噪引入的階梯誤差。通過對實(shí)驗(yàn)數(shù)據(jù)進(jìn)行計算驗(yàn)證,將所提算法與已有方法進(jìn)行了對比,結(jié)果證明了所提方法的優(yōu)越性。
關(guān)鍵詞:局部放電;去噪;小波變換;閾值去噪;全變差
中圖分類號:TM866 文獻(xiàn)標(biāo)識碼:A 文章編號:1007-3175(2020)11-0016-07
Combined Partial Discharge Signal Denoising Algorithm Based on Wavelet Transform and Total Variation
DAI Yu1,2, WANG Lu-liang1,3, YANG Xu4,5, ZHANG Jing4, ZHOU Si-yuan5, PAN Zi-jun5, YAO Yu-hang5
(1 Electric Power Research Institute of Hainan Power Grid Limited Company, Haikou 570311 , China;
2 School of Civil Engineering and Architecture, Northeast Electric Power University, Jilin 132012,China;
3 Hainan Key Laboratory of Physical and Chemical Analysis of Power Grid, Haikou 570311 ,China;
4 Wuhan NARI Limited Liability Company of State Grid Electric Power Research Institute, Wuhan 430074, China;
5 School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)
Abstract: On-site measurement partial discharge (Partial Discharge, PD) signal will be polluted by white noise, and it is necessary to denoise it. Based on wavelet transform threshold denoising and total variation denoising method, a combined wavelet threshold and total variation denoising algorithm is proposed. The algorithm merges the two methods, absorbs their respective advantages, effectively reduces the fluctuation error of the PD signal due to wavelet threshold denoising, and avoids the step error introduced by the total variation denoising. By calculating and verifying the experimental data, the proposed algorithm is compared with the existing method, and the result proves the superiority of the proposed method.
Key words: partial discharge; denoising; wavelet transform; threshold denoising; total variation
參考文獻(xiàn)
[1] 黃超,魏本剛,任曉明,等. 基于不同放電模型的變壓器局部放電光學(xué)特性研究[J]. 電測與儀表,2016,53(20):108-113.
[2] 唐炬,佘新,萬凌云,等. 負(fù)極性直流局部放電量與SF6分解過程的關(guān)聯(lián)特性[J].中國電機(jī)工程學(xué)報,2018,38(2):628-636.
[3] 周瑋,張傳計,張軍,等. 局部放電UHF檢測儀校準(zhǔn)方法研究[J]. 電測與儀表,2016,53(13):100-106.
[4] 牛博,張欣宜,李亞峰,等. 基于特高頻法識別配電站房開關(guān)柜的局部放電類型研究[J]. 電測與儀表,2019,56(23):43-47.
[5] 王永強(qiáng),李長元,胡芳芳,等. 基于改進(jìn)EMD的GIS局部放電特高頻信號降噪方法研究[J]. 電測與儀表,2017,54(9):1-5.
[6] 代蕩蕩,王先培,龍嘉川,等. 基于改進(jìn)Protrugram和小波變換的超高頻局部放電信號去噪方法[J].高電壓技術(shù),2018,44(11):3577-3586.
[7] 周凱,黃永祿,謝敏,等. 短時奇異值分解用于局放信號混合噪聲抑制[J]. 電工技術(shù)學(xué)報,2019,34(11):2435-2443.
[8] LI J, CHENG C, JIANG T, et al. Wavelet denoising of partial discharge signals based on genetic adaptive threshold estimation[J]. IEEE Transactions on Dielectrics & Electrical Insulation,2012,19(2):543-549.
[9] 張曉星,周君杰,李楠,等. 抑制局部放電白噪聲的分塊閾值空域相關(guān)聯(lián)合去噪法[J]. 高電壓技術(shù),2011,37(5):1142-1148.
[10] 葉會生,陳曉林,周挺,等. 提升雙樹復(fù)小波在GIS局部放電監(jiān)測白噪聲抑制的應(yīng)用[J]. 高電壓技術(shù),2017,43(3):851-858.
[11] WEICKERT T, BENJAMINSEN C, KIENCKE U.Analytic wavelet packets-combining the dual-tree approach with wavelet packets for signal analysis and filtering[J].IEEE Transactions on Signal Processing,2009,57(2):493-502.
[12] SELESNICK I W, BARANIUK R G, KINGSBURY N C.The dual-tree complex wavelet transform[J].IEEE Signal Processing Magazine,2005,22(6):123-151.
[13] SELESNICK I W.The design of approximate Hilbert transform pairs of wavelet bases[J]. IEEE Transactions on Signal Processing,2002,50(5):1144-1152.
[14] SELESNICK I W.Hilbert transform pairs of wavelet bases[J].IEEE Signal Processing Letters,2001,8(6):170-173.
[15] 解頤. 基于多尺度分析的全變差去噪和壓縮感知研究[D]. 北京:北京交通大學(xué),2017.
[16] 張雨. 基于全變差的圖像融合與乘性去噪方法研究[D]. 哈爾濱:哈爾濱工業(yè)大學(xué),2018.
[17] DONOHO D L.Denoising by soft-thresholding[J]. IEEE Transactions on Information Theory,1995,41(3):613-627.
[18] 張賢達(dá). 現(xiàn)代信號處理[M].3 版. 北京:清華大學(xué)出版社,2002.
[19] RUDIN L I, OSHER S, FATEMI E.Nonlinear total variation based noise removal algorithms[J]. Physica D:Nonlinear Phenomena,1992,60(1):259-268.
[20] OTTERSTEN J, WAHLBERG B, ROJAS C R.Accurate Changing Point Detection for l1 Mean Filtering[J].IEEE Signal Processing Letters,2016,23(2):297-301.
[21] FIGUEIREDO M A T, BIOUCAS-DIAS J M, NOWAK R D. Majorization-minimization algorithms for wavelet-based image restoration[J].IEEE Transactions on Image Processing,2007,16(12):2980-2991.
[22] 唐炬,張曉星,曾福平. 組合電器設(shè)備局部放電特高頻監(jiān)測與故障診斷[M]. 北京:科學(xué)出版社,2016.