基于自适应VMD和A-SVD的MOA在线监测方法

doi:10.11930/j.issn.1004-9649.202103144

中国电力 ›› 2021, Vol. 54 ›› Issue (10): 177-185.DOI: 10.11930/j.issn.1004-9649.202103144

基于自适应VMD和A-SVD的MOA在线监测方法

阮莹¹, 叶行汶¹, 邓明锋¹, 王星^2,3, 杨林禹³, 舒勤³

1. 国网福建省电力公司电力科学研究院，福建福州 350007;
2. 国网陕西省电力公司西安供电公司，陕西西安 710032;
3. 四川大学电气工程学院，四川成都 610065

收稿日期:2021-03-26 修回日期:2021-05-30 出版日期:2021-10-05 发布日期:2021-10-16
作者简介:阮莹(1989-),女,工程师,从事电力设备在线监测及其诊断研究,E-mail:djry132@sina.com;杨林禹(1995-),男,通信作者,硕士研究生,从事避雷器的在线监测和故障诊断研究,E-mail:897998913@qq.com
基金资助:
国家自然科学基金资助项目(51877141)；国网福建省电力有限公司科技项目(避雷器实时监测数据的边缘计算方法与监测装置抗干扰技术应用研究，52130419001T)

A New Online Monitoring Method for MOA Based on A-VMD and A-SVD

RUAN Ying¹, YE Xingwen¹, DENG Mingfeng¹, WANG Xing^2,3, YANG Linyu³, SHU Qin³

1. Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China;
2. State Grid Shaanxi Xi'an Power Supply Company, Xian 710032, China;
3. College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Received:2021-03-26 Revised:2021-05-30 Online:2021-10-05 Published:2021-10-16
Supported by:
This work is supported by National Natural Science Foundation of China ( No.51877141 ) and the Science and Technology Project of State Grid Fujian Electric Power Co., Ltd. (Research on Edge Calculation Method of Real-Time Measured Data of Arrester and Application of Interference Suppression Technology of Monitoring Device, No.52130419001T)

摘要/Abstract

摘要： 阻性电流是反映金属氧化物避雷器(MOA)绝缘状态的关键参数。电力系统正常运行时的MOA阻性电流非常小，阻性电流实测有效值数据易受到高频噪声、白噪声、随机脉冲等干扰影响，造成报警装置误报警。然而，现有方法难以有效消除上述干扰对阻性电流的影响，因此提出了一种基于自适应变分模态分解 (adaptive variational mode decomposition，A-VMD)和自适应奇异值分解(adaptive singular value decomposition，A-SVD)的MOA在线监测方法。首先，通过顺序递增二次惩罚因子和分解层数值，以能量和损失指标衡量VMD分解的效果，寻找分解层数和二次惩罚因子的最优参数值。然后，采用A-SVD消除经A-VMD初步去噪后阻性电流中少量残留白噪声，为监测MOA绝缘状态提供可靠依据。仿真试验和实测数据均证明了该方法的有效性，处理结果满足实际工程的需求。

关键词: 金属氧化物避雷器, 阻性电流, 在线监测, A-VMD, A-SVD

Abstract: Resistive current is a key parameter to judge the working state of a metal oxide arrester (MOA). The resistive current of a MOA is very small when the power system is working normally, and the measured resistive current is prone to be affected by the interference of high-frequency noise, white noise and random pulses, leading to the false alarms of alarming devices. At present, none of the existing denoising methods can completely eliminate the influence of the above interference on the resistive current of MOA. Therefore, this paper proposes a new method for eliminating the interference of resistive current of MOA based on adaptive variational mode decomposition (A-VMD) and adaptive singular value decomposition (A-SVD). Firstly, by sequentially changing the secondary penalty factor and the decomposition layer, and with the energy and loss indicators to measure the effect of VMD decomposition, the optimal parameters of decomposition layers and secondary penalty factor are searched out. Secondly, the A-SVD is used to eliminate the residual white noise in the resistive current after preliminary denoising by A-VMD, which provides a reliable basis for judging the insulating state of a MOA. The effectiveness of the proposed method is verified by simulation and measured data, and the processing results meet the requirements of actual projects.

Key words: metal oxide arrester, resistive current, online monitoring, adaptive variational mode decomposition, adaptive singular value decomposition

阮莹, 叶行汶, 邓明锋, 王星, 杨林禹, 舒勤. 基于自适应VMD和A-SVD的MOA在线监测方法[J]. 中国电力, 2021, 54(10): 177-185.

RUAN Ying, YE Xingwen, DENG Mingfeng, WANG Xing, YANG Linyu, SHU Qin. A New Online Monitoring Method for MOA Based on A-VMD and A-SVD[J]. Electric Power, 2021, 54(10): 177-185.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202103144

https://www.electricpower.com.cn/CN/Y2021/V54/I10/177

参考文献

[1] 李海峰, 华永东, 姜燕, 等. 基于多频点插值和三维曲面建模的MOA阻性电流测量方法[J]. 高电压技术, 2019, 45(11): 3512–3520
LI Haifeng, HUA Yongdong, JIANG Yan, et al. New measurement method for MOA resistive current based on multi-frequency points interpolation and three-dimensional surface modeling[J]. High Voltage Engineering, 2019, 45(11): 3512–3520
[2] ZHAO T, LI Q M, QIAN J L. Investigation on digital algorithm for on-line monitoring and diagnostics of metal oxide surge arrester based on an accurate model[J]. IEEE Transactions on Power Delivery, 2005, 20(2): 751–756.
[3] 王星, 阮莹, 陈敏维, 等. 基于EMD-WPT-SVD和指数加权平均的MOA阻性电流去干扰方法[J/OL].高电压技术, 2020: 1–10. https://doi.org/10.13336/j.1003-6520.hve.20201062.
WANG Xing, RUAN Ying, CHEN Minwei, et al. Method for eliminating MOA resistive current interference based on EMD-WPT-SVD and exponential weighted average[J/OL]. High Voltage Engineering, 2020: 1–10. https://doi.org/10.13336/j.1003-6520.hve.20201062.
[4] 魏新劳, 郑文雷. 以中相电压过零点为时间基点的氧化锌避雷器阻性电流提取方法[J]. 电工技术学报, 2016, 31(20): 209–218
WEI Xinlao, ZHENG Wenlei. An extracting method of resistive leakage current for zinc-oxide surge arrester with time base set at zero-point of middle phase’s voltage[J]. Transactions of China Electrotechnical Society, 2016, 31(20): 209–218
[5] 宫建立. 金属氧化物避雷器阻性电流测量方法研究[D].上海: 上海交通大学, 2007.
GONG Jianli. Research on resistance current measurement method for metal oxide arrester[D]. Shanghai: Shanghai Jiao Tong University, 2007.
[6] 李海峰, 刘俊勇, 徐斌, 等. 消除外部环境因素干扰的MOA在线监测参数修正方法[J]. 高电压技术, 2018, 44(8): 2580–2586
LI Haifeng, LIU Junyong, XU Bin, et al. The modified method of MOA on-line monitoring parameters by eliminating external environmental factors interference[J]. High Voltage Engineering, 2018, 44(8): 2580–2586
[7] 李小建. 金属氧化物避雷器电流基本参数分析[J]. 高电压技术, 2008, 34(1): 37–40
LI Xiaojian. Analyzing the basic parameters of currents of metal oxide surge arrester[J]. High Voltage Engineering, 2008, 34(1): 37–40
[8] 黄建才, 朱永利. 基于FFT和ITD的绝缘子泄漏电流去噪[J]. 电力自动化设备, 2013, 33(10): 101–106
HUANG Jiancai, ZUU Yongli. Denoising of insulator leakage current based on FFT and ITD[J]. Electric Power Automation Equipment, 2013, 33(10): 101–106
[9] 杨文宇, 杨俊杰, 杨旭英. 基于数学形态学的绝缘子在线监测[J]. 高电压技术, 2008, 34(11): 2317–2321
YANG Wenyu, YANG Junjie, YANG Xuying. New method for online monitoring of insulators based on mathematical morphology[J]. High Voltage Engineering, 2008, 34(11): 2317–2321
[10] 王文浩, 何文林, 董学松, 等. 金属氧化物避雷器泄漏电流在线监测数据自适应滤波算法[J]. 高压电器, 2013, 49(9): 91–94
WANG Wenhao, HE Wenlin, DONG Xuesong, et al. Adaptive filtering algorithm for on-line monitoring of leakage current of metal oxide arrester[J]. High Voltage Apparatus, 2013, 49(9): 91–94
[11] 董莉娜, 胡可, 王微波, 等. 小波消噪在10 kV金属氧化物避雷器在线检测中的应用[J]. 高电压技术, 2014, 40(3): 891–896
DONG Lina, HU Ke, WANG Weibo, et al. Application of wavelet denoise in on-line detection of 10 kV metal oxide arresters[J]. High Voltage Engineering, 2014, 40(3): 891–896
[12] 黄建才, 朱永利. 采用小波变换的绝缘子泄漏电流去噪[J]. 高电压技术, 2012, 38(8): 1981–1987
HUANG Jiancai, ZHU Yongli. Insulator leakage current de-nosing using wavelet transform[J]. High Voltage Engineering, 2012, 38(8): 1981–1987
[13] 李璟延, 司马文霞, 姚陈果, 等. 染污绝缘子安全区泄漏电流检测中去除信号干扰方法[J]. 电力系统自动化, 2008, 32(5): 85–89
LI Jingyan, SIMA Wenxia, YAO Chenguo, et al. Signal interference removal method in leakage current detection of contaminated insulator safety zone[J]. Automation of Electric Power Systems, 2008, 32(5): 85–89
[14] KOPSINIS Y, MCLAUGHLIN S. Development of EMD-based denoising methods inspired by wavelet thresholding[J]. IEEE Transactions on Signal Processing, 2009, 57(4): 1351–1362.
[15] 朱文龙, 周建中, 肖剑, 等. 独立分量分析-经验模态分解特征取在水电机组振动信号中的应用[J]. 中国电机工程学报, 2013, 33(29): 95–101
ZHU Wenlong, ZHOU Jianzhong, XIAO Jian, et al. An ICA-EMD feature extraction method and its application to vibration signals of hydroelectric generating units[J]. Proceedings of the CSEE, 2013, 33(29): 95–101
[16] 赵腾, 姚毅, 周芯竹. 基于EEMD阈值的MOA避雷器泄漏电流去噪的研究[J]. 科学技术与工程, 2014, 14(31): 231–236
ZHAO Teng, YAO Yi, ZHOU Xinzhu, et al. Research on de-noising of MOA leakage current based on EEMD threshold[J]. Science Technology and Engineering, 2014, 14(31): 231–236
[17] 杨冬锋, 陈盛开, 刘晓军, 等. 基于自适应VMD和时-频分段能量熵特征的过电压信号识别[J]. 电网技术, 2019, 43(12): 4597–4604
YANG Dongfeng, CHEN Shengkai, LIU Xiaojun, et al. Research on overvoltage signal recognition based on adaptive VMD and time-frequency segment energy entropy[J]. Power System Technology, 2019, 43(12): 4597–4604
[18] 卿宗胜, 高云鹏, 吴聪, 等. 基于自适应VMD和改进功率谱估计的球磨机负荷特征提取[J]. 仪器仪表学报, 2020, 41(5): 234–241
QING Zongsheng, GAO Yunpeng, WU Cong, et al. Feature extraction method of ball mill load based on the adaptive variational mode decomposition and the improved power spectrum analysis[J]. Chinese Journal of Scientific Instrument, 2020, 41(5): 234–241
[19] 钱国超, 彭庆军, 程志万, 等. 基于参数自适应VMD和SA-ELM的有载分接开关故障诊断[J]. 电力工程技术, 2020, 39(1): 157–164
QIAN Guochao, PENG Qingjun, CHENG Zhiwan, et al. Fault diagnosis of on-load tap-changer based on the parameter-adaptive VMD and SA-ELM[J]. Electric Power Engineering Technology, 2020, 39(1): 157–164
[20] 周凯, 黄永禄, 谢敏, 等. 短时奇异值分解用于局放信号混合噪声抑制[J]. 电工技术学报, 2019, 34(11): 2435–2443
ZHOU Kai, HUANG Yonglu, XIE Min, et al. Mixed noises suppression of partial discharge signal employing short-time singular value decomposition[J]. Transactions of China Electrotechnical Society, 2019, 34(11): 2435–2443
[21] 孙飒爽, 李珊君, 舒勤, 等. 基于截断奇异值分解的局部放电去噪方法[J/OL].电网技术, 2020: 1–8. https://kns.cnki.net/kcms/detail/11.2410.TM.20200707.1656.011.html.
SUN Sashuang, LI Shanjun, SHU Qin, et al. A partial discharge denoising method based on piecewise singular value decomposition[J/OL]. Power System Technology, 2020: 1–8. https://kns.cnki.net/kcms/detail/11.2410.TM.20200707.1656.011.html.
[22] WANG Z P, LU C, WANG Z L, et al. Fault diagnosis and health assessment for bearings using the Mahalanobis–Taguchi system based on EMD-SVD[J]. Transactions of the Institute of Measurement and Control, 2013, 35(6): 798–807.
[23] XIE J, LV F, LI M, et al. Suppressing the discrete spectral interference of the partial discharge signal based on bivariate empirical mode decomposition[J]. International Transactions on Electrical Energy Systems, 2017, 27(10): e2407.
[24] 律方成, 谢军, 王永强. 局部放电信号稀疏表示去噪方法[J]. 中国电机工程学报, 2015, 35(10): 2625–2633
LÜ Fangcheng, XIE Jun, WANG Yongqiang. Local discharge signal sparse representation de-noising method[J]. Proceedings of the CSEE, 2015, 35(10): 2625–2633

基于自适应VMD和A-SVD的MOA在线监测方法

A New Online Monitoring Method for MOA Based on A-VMD and A-SVD

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	李楠, 李松原, 尚学军, 郭博文, 李琳, 祖国强, 侯恺. 基于交叉比对的UHF-PD在线监测装置现场校验方法[J]. 中国电力, 2022, 55(4): 100-107.
[2]	朱永灿, 张鹏, 田毅, 黄新波. 变电设备在线监测技术中的同步采集触发方法对比分析[J]. 中国电力, 2022, 55(3): 64-73.
[3]	刘浩然, 周凯, 王昱皓, 李蓉, 傅尧, 饶显杰. 基于电磁耦合注入的电力电缆局部缺陷在线定位方法[J]. 中国电力, 2021, 54(12): 177-185,194.
[4]	唐酿, 陶炳权, 曲皓玥, 胡妹林, 蔡昌松, 黄明欣. 基于GaN器件的高电压线路多米诺无线供电系统设计[J]. 中国电力, 2020, 53(5): 69-76.
[5]	蒋晶, 赵洋洋, 樊宏伟, 周振宇, 董朝阳, 杨青波, 王晓丽. 图像识别在特高压换流阀元件故障在线监测系统的应用[J]. 中国电力, 2020, 53(11): 126-132.
[6]	余斌, 尹项根, 吴小忠, 汤吉鸿, 宁春海, 李辉, 朱维钧, 刘海峰, 徐浩. 输电线路在线监测的层次化通信网络规划模型[J]. 中国电力, 2019, 52(3): 161-168.
[7]	王河, 李跃东, 秦志国, 尹晓晖, 马素霞, 王芳, 张嘉杰. 基于空冷岛性能在线监测的风机优化调节[J]. 中国电力, 2018, 51(8): 154-161.
[8]	梁可道. 粒子群优化的小波算法在避雷器老化诊断中的应用[J]. 中国电力, 2018, 51(6): 102-106.
[9]	韦景, 莫小向, 程凤芝, 丁五行. SF₆电气设备的SO₂在线监测装置研究[J]. 中国电力, 2018, 51(2): 33-38.
[10]	高燃, 束畅, 蒲道杰, 尹建军, 朱雨晨, 黄道均, 吴大胜. 1 000 kV特高压避雷器泄漏电流异常缺陷分析[J]. 中国电力, 2017, 50(5): 59-64.
[11]	胡边, 万元, 唐伟, 苏明旭. 电厂调速器油系统颗粒度的光散射法在线监测[J]. 中国电力, 2017, 50(4): 135-140.
[12]	盛锴, 周年光, 宋军英, 雷滨, 彭双剑, 寻新. 再热凝汽式汽轮机阀门流量特性在线监测优化[J]. 中国电力, 2017, 50(12): 62-67.
[13]	谈博, 陈志刚, 顾涛, 赵志丹, 俞静, 曹琛. 机组一次调频特性在线监测方法[J]. 中国电力, 2017, 50(12): 68-74.
[14]	周洪，蒋燕. 应用于智能电网在线监测设备供电的磁共振无线供能装置[J]. 中国电力, 2016, 49(5): 111-115.
[15]	何石鱼，赵会民，刘长东，胡晨晖. 燃煤汞污染控制研究进展[J]. 中国电力, 2016, 49(2): 170-175.

模态框（Modal）标题

基于自适应VMD和A-SVD的MOA在线监测方法

A New Online Monitoring Method for MOA Based on A-VMD and A-SVD

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics