基于图像信息熵与多元变分模态分解的电缆局放信号去噪方法

doi:10.19595/j.cnki.1000-6753.tces.230638

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Abstract In recent years, cross-linked polyethylene cables have been developed in transmission lines and urban distribution networks due to their advantages, lightweight, high-temperature resistance, and high transmission power. With the increase in the number of XLPE power cables put into operation and the extension of cable lines, silicone rubber insulated prefabricated intermediate joints are widely used in XLPE power cables due to their excellent high-voltage shielding performance, overall prefabricated design, reliable grounding, large voltage margin, and convenient on-site installation. The intermediate cable joint is usually installed and formed on the laying site, which can quickly leave a hidden danger of cable operation failure. Because it comprises multi-layer solid composite structures with different dielectric properties, the probability of accidents is much higher than that of the cable body.
Partial discharge (PD) detection is the main means of evaluating the insulation status of XLPE cables and the manufacturing and installation defects of cables. A PD denoising method based on image information entropy and novel adaptive multivariate variational mode decomposition (MVMD) is proposed to address the issues of white noise, periodic narrowband interference, and poor adaptability in on-site detection of PD at cable terminals and intermediate joints. Firstly, optimize the parameters of the MVMD algorithm by integrating multiple factors, and then, based on parameter optimization, perform modal decomposition on the noisy PD signal. Secondly, the kurtosis of each eigenmode component is calculated, and the kurtosis of the sine signal and double exponential decay signal at the signal-to-noise ratio of 0dB is calculated by using the characteristic that kurtosis is sensitive to noise to distinguish the PD characteristic information from the noise interference component. Then, the 3σ criterion is used to filter white noise with normal distribution. Finally, based on the improved new wavelet threshold function, the reconstructed PD signal is denoised to obtain the denoised PD signal.
The following conclusions can be drawn by comparing the method with other denoising algorithms: (1) The Spearman variational mode decomposition (S-VMD) can improve modal aliasing, but there is still residual noise in the denoised signal. Hence, the denoising effect is not ideal. (2) The novel adaptive ensemble empirical mode decomposition (NAEEMD) cannot wholly eliminate modal aliasing, resulting in a certain degree of displacement of the discharge starting position and affecting subsequent diagnosis and positioning. (3) Although the short-time Fourier transform and matrix factorization (STFT-SVD) can effectively suppress white noise and periodic narrowband interference, the denoised PD signal contains residual noise, and the execution efficiency of this algorithm is low. (4) By calculating various evaluation indicators, the method has a good denoising effect on the on-site noisy PD signal. At the same time, this method has the advantages of less time consumption and high execution efficiency.
The following conclusions can be drawn: (1) The information entropy is used to measure the aggregation characteristics of the gray image distribution accurately and then to determine the certainty of the PD pulse signal.
By constructing the information entropy of grayscale, the mode aliasing phenomenon of empirical mode decomposition (EMD), and other algorithms is overcome, and the accurate decomposition of noisy PD signals can be achieved, thus achieving accurate feature extraction. (2) Distinguish PD features from noise interference by calculating kurtosis values. Using the characteristic that kurtosis is sensitive to noise, the kurtosis value of the sine signal and double exponential decay signal at SNR=0 dB is calculated to accurately distinguish PD feature information and noise interference component, which lays a foundation for improving the denoising effect of PD signal. At the same time, filtering out noise interference components largely compresses data, reducing algorithm time consumption and improving execution efficiency. (3) The denoising effect of noisy PD signals on site has been improved by improving the wavelet threshold function and the threshold value. Combining the number of wavelet decomposition layers with the general threshold setting, a new type of wavelet threshold function with exponential decay is constructed, which corresponds to the mathematical model of XLPE cable PD signal, and then the detail coefficients of each layer after wavelet decomposition are accurately obtained, to improve the denoising effect of on-site noisy PD signals.

Key words： Multivariate variational mode decomposition partial discharge image information entropy kurtosis power cables

Received: 08 May 2023

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	Wang Xiaowei
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	Wang Yizhao
	Zhang Zhihua
	Liang Zhenfeng

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Wang Xiaowei,Wang Xue,Wang Yizhao等. A Denoising Algorithm for Cable Partial Discharge Signals Based on Image Information Entropy and Multivariate Variational Mode Decomposition[J]. Transactions of China Electrotechnical Society, 2024, 39(13): 4100-4115.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.230638 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I13/4100

[1] 常文治. 电力电缆中间接头典型缺陷局部放电发展过程的研究[D]. 北京: 华北电力大学, 2013.
Chang Wenzhi.Study on development of typical partial discharges in power cables joint[D]. Beijing: North China Electric Power University, 2013.
[2] Pan Cheng, Chen G, Tang Ju, et al.Numerical modeling of partial discharges in a solid dielectric-bounded cavity: a review[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2019, 26(3): 981-1000.
[3] Ashtiani M B, Shahrtash S M.Partial discharge de-noising employing adaptive singular value decomposition[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2014, 21(2): 775-782.
[4] 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.
[5] 饶显杰, 周凯, 汪先进, 等. 基于改进SVD算法的局部放电窄带干扰抑制方法[J]. 高电压技术, 2021, 47(2): 705-713.
Rao Xianjie, Zhou Kai, Wang Xianjin, et al.Suppression of narrow-band noise of partial discharge based on improved SVD algorithm[J]. High Voltage Engineering, 2021, 47(2): 705-713.
[6] Yan Yuan, Trinchero R, Stievano I S, et al.An automatic tool for partial discharge de-noising via short-time Fourier transform and matrix factorization[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-12.
[7] Chan J C, Ma Hui, Saha T K, et al.Self-adaptive partial discharge signal de-noising based on ensemble empirical mode decomposition and automatic morphological thresholding[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2014, 21(1): 294-303.
[8] Kedadouche M, Thomas M, Tahan A.A comparative study between empirical wavelet transforms and empirical mode decomposition methods: application to bearing defect diagnosis[J]. Mechanical Systems and Signal Processing, 2016, 81: 88-107.
[9] Jin Tao, Li Qiangguang, Mohamed M A.A novel adaptive EEMD method for switchgear partial discharge signal denoising[J]. IEEE Access, 2019, 7: 58139-58147.
[10] Dragomiretskiy K, Zosso D.Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
[11] 马星河, 孔卫东, 李自强, 等. 一种基于S_VMD与Sdr_SampEn的局部放电信号去噪方法[J]. 电力系统保护与控制, 2022, 50(18): 29-38.
Ma Xinghe, Kong Weidong, Li Ziqiang, et al.A denoising method for a partial discharge signal based on S_VMD and Sdr_SampEn[J]. Power System Protection and Control, 2022, 50(18): 29-38.
[12] Hussein R, Shaban K B, El-Hag A H. Wavelet transform with histogram-based threshold estimation for online partial discharge signal denoising[J]. IEEE Transactions on Instrumentation and Measurement, 2015, 64(12): 3601-3614.
[13] Tang Ju, Zhou Siyuan, Pan Cheng.A denoising algorithm for partial discharge measurement based on the combination of wavelet threshold and total variation theory[J]. IEEE Transactions on Instrumen-tation and Measurement, 2020, 69(6): 3428-3441.
[14] Zhou Siyuan, Tang Ju, Pan Cheng, et al.Partial discharge signal denoising based on wavelet pair and block thresholding[J]. IEEE Access, 2020, 8: 119688-119696.
[15] 徐永干, 姜杰, 唐昆明, 等. 基于Hankel矩阵和奇异值分解的局部放电窄带干扰抑制方法[J]. 电网技术, 2020, 44(7): 2762-2769.
Xu Yonggan, Jiang Jie, Tang Kunming, et al.A method of suppressing narrow-band interference in partial discharge based on Hankel matrix and singular value decomposition[J]. Power System Technology, 2020, 44(7): 2762-2769.
[16] 揭青松, 杨庆, 崔浩楠, 等. 基于暂态电压传递特性的电缆接头绝缘状态检测方法[J]. 高电压技术, 2022, 48(3): 1124-1132.
Jie Qingsong, Yang Qing, Cui Haonan, et al.Insulation state detection method of cable joint based on transient voltage transfer characteristics[J]. High Voltage Engineering, 2022, 48(3): 1124-1132.
[17] 唐炬, 高丽, 谢颜斌, 等. 复小波包变换抑制PD监测中周期性窄带干扰[J]. 高电压技术, 2008, 34(11): 2355-2361.
Tang Ju, Gao Li, Xie Yanbin, et al.Suppressing PD’s periodicity narrow band noise in the PD measurement by using complex wavelet packet transform[J]. High Voltage Engineering, 2008, 34(11): 2355-2361.
[18] Zhong Jun, Bi Xiaowen, Shu Qin, et al.Partial discharge signal denoising based on singular value decomposition and empirical wavelet transform[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(11): 8866-8873.
[19] 唐炬, 黄江岸, 张晓星, 等. 局部放电在线监测中混频周期性窄带干扰的抑制[J]. 中国电机工程学报, 2010, 30(13): 121-127.
Tang Ju, Huang Jiangan, Zhang Xiaoxing, et al.Suppression of the periodic narrow-band noise with mixed frequencies in partial discharge on-line monitoring[J]. Proceedings of the CSEE, 2010, 30(13): 121-127.
[20] 李文华, 姜惠, 赵正元, 等. 基于波形匹配端点延拓法优化的经验模态分解算法在铁路继电器参数降噪上的应用[J]. 电工技术学报, 2022, 37(10): 2656-2664.
Li Wenhua, Jiang Hui, Zhao Zhengyuan, et al.Application of empirical mode decomposition algorithm based on waveform matching endpoint continuation method in noise reduction of railway relay parameters[J]. Transactions of China Electrotechnical Society, 2022, 37(10): 2656-2664.
[21] Ghorat M, Gharehpetian G B, Latifi H, et al.A new partial discharge signal denoising algorithm based on adaptive dual-tree complex wavelet transform[J]. IEEE Transactions on Instrumentation and Measure-ment, 2018, 67(10): 2262-2272.
[22] 杨童亮, 胡东, 唐超, 等. 基于SMA-VMD-GRU模型的变压器油中溶解气体含量预测[J]. 电工技术学报, 2023, 38(1): 117-130.
Yang Tongliang, Hu Dong, Tang Chao, et al.Prediction of dissolved gas content in transformer oil based on SMA-VMD-GRU model[J]. Transactions of China Electrotechnical Society, 2023, 38(1): 117-130.
[23] 徐黄宽, 张黎, Bilal Iqbal Ayubi, 等. 基于改进变分模态分解去噪的高频电应力下聚酰亚胺局部放电温-频特性研究[J]. 电工技术学报, 2023, 38(3): 565-576.
Xu Huangkuan, Zhang Li, Ayubi B I, et al.Study on temperature-frequency partial discharge characteristics of polyimide under high frequency electrical stress based on improved variational modal decomposition denoising[J]. Transactions of China Electrotechnical Society, 2023, 38(3): 565-576.
[24] 刘灏, 商峻, 毕天姝, 等. 基于实测数据的电网频率信号特征分析与提取方法[J]. 电力系统自动化, 2023, 47(10): 135-144.
Liu Hao, Shang Jun, Bi Tianshu, et al.Feature analysis and extraction method of power grid frequency signal based on measured data[J]. Automation of Electric Power Systems, 2023, 47(10): 135-144.
[25] 唐炬, 魏钢, 李伟, 等. 基于双向二维最大间距准则的局部放电灰度图像特征提取[J]. 电网技术, 2011, 35(3): 129-134.
Tang Ju, Wei Gang, Li Wei, et al.Partial discharge gray image feature extraction based on bi-directional two-dimensional maximum margin criterion[J]. Power System Technology, 2011, 35(3): 129-134.
[26] 毕潇文, 钟俊, 张大堃, 等. 基于改进奇异值与经验小波分解的局放去噪算法[J]. 电网技术, 2021, 45(12): 4957-4963.
Bi Xiaowen, Zhong Jun, Zhang Dakun, et al.Improved singular value and empirical wavelet decomposition algorithm in partial discharge denoising[J]. Power System Technology, 2021, 45(12): 4957-4963.
[27] Wang Qiusheng, Kundur D, Yuan Haiwen, et al.Noise suppression of corona current measurement from HVdc transmission lines[J]. IEEE Transactions on Instrumentation and Measurement, 2016, 65(2): 264-275.
[28] 夏琴, 肖洒, 周刚, 等. 改进VMD和提升小波在局部放电去噪中的应用[J]. 电气自动化, 2022, 44(6): 46-48, 52.
Xia Qin, Xiao Sa, Zhou Gang, et al.Application of improved variational mode decomposition and lifting wavelet method in partial discharge denoising[J]. Electrical Automation, 2022, 44(6): 46-48, 52.
[29] 王维博, 董蕊莹, 曾文入, 等. 基于改进阈值和阈值函数的电能质量小波去噪方法[J]. 电工技术学报, 2019, 34(2): 409-418.
Wang Weibo, Dong Ruiying, Zeng Wenru, et al.A wavelet de-noising method for power quality based on an improved threshold and threshold function[J]. Transactions of China Electrotechnical Society, 2019, 34(2): 409-418.
[30] 沈谢林, 王利, 郭建钊, 等. 10 kV配电电缆绝缘耐压与局放一体化测试方法研究[J]. 高压电器, 2023, 59(2): 120-126.
Shen Xielin, Wang Li, Guo Jianzhao, et al.Research on integrated method for withstanding voltage and partial discharge test of 10 kV power cable insulation[J]. High Voltage Apparatus, 2023, 59(2): 120-126.