基于虚拟通道延拓的局部放电信号压缩采样方法

doi:10.19595/j.cnki.1000-6753.tces.220372

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摘要

针对局部放电（局放）信号频域非带限且要求采样频率过高而引发的电路参数与性能矛盾,提出一种基于虚拟通道延拓的局部放电信号压缩采样方法,利用压缩感知并行测量来降低采样频率。通过在采样电路中实现虚拟通道延拓,降低了并行测量通道数,优化了压缩采样电路结构。为了使测量通道参数适合局放信号特征,提出了基于非相干性延拓的三值观测矩阵设计算法,包括引入自相干正则项和利用时序加窗差值的变步长梯度下降法来降低参数相干性。最后,将观测矩阵用于虚拟通道延拓电路,实现局放信号的压缩采样。结果表明,文中的压缩采样方法将采样频率降低至1.6 MHz,利用多通道测量将整体采样频率和数据量分别压缩至48%和14%,将实际并行测量通道数优化至12 条。该文的压缩采样方法降低了传感设备的元器件参数要求和电路结构复杂度,达到了电力设备局放信号在线监测的需求。

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关键词 ：局部放电, 压缩感知, 观测矩阵, 非相干性, 虚拟通道延拓

Abstract：

Partial discharge (PD) is the important parameter to monitor the operating status of power equipment. However, PD signals are non-band-limited in the frequency domain, and the sensor theoretically needs to be equipped with the analog-to-digital converter (ADC) with infinitely high sampling frequency. The cost of ADCs increases exponentially with the sampling frequency due to production process limitations. Aiming at this circuit parameter and performance contradiction, the compressive sensing (CS) theory is introduced to achieve compressed sampling of PD signals from power equipment. A compressed sampling method for PD signals based on virtual channel delay topology is proposed to reduce the sampling frequency by using compressed sensing parallel measurement.
Firstly, Analog-to-information converter for PD signal based on virtual channel extension (VCE-AIC) is proposed in combination with PD signals characteristics. Parallel measurements are used to reduce the sampling frequency and to create virtual measurement channels based on non-coherent extension of submeasurement segments. The actual number of measurement channels is reduced, and the compressed sampling circuit structure is optimized. After that, according to the characteristics of PD signals, channel delay requirements and noncoherence criterion, a three-valued observation matrix design algorithm based on noncoherent delay is proposed. It includes the introduction of a self-consistent regular term and a variable-step gradient descent metho using the time-series plus window difference to reduce the parameter coherence. Finally, the observation matrix is used in the virtual channel delay topology circuit to realize the compressed sampling of PD signals.
The reconstruction accuracy of the compressed measurement signal is guaranteed from the following six perspectives. (Ⅰ) The observation matrix is designed to satisfy the RIP criterion, i.e., the observation matrix has low coherence with the sparse dictionary. (Ⅱ) The proposed three-valued observation matrix design algorithm based on non-coherent extension for VCE-AIC. (Ⅲ) Maximize the completeness and generality of the sparse dictionary for PD signals. (Ⅳ) Adopting orthogonal matching tracking algorithm for signal reconstruction. (Ⅴ) The generally common PD signals waveform evaluation index is selected. (Ⅵ) The complete theoretical and experimental analysis is set up for the selection of each parameter.
Sampling analysis is performed based on the measured PD signals. The results show that the compressed sampling method reduces the sampling frequency to 1.6 MHz. The overall sampling frequency and data volume are compressed to 48% and 14%, respectively, using multi-channel measurements. The actual number of parallel measurement channels is optimized to 12.
The following conclusions can be drawn from the analysis of the experimental results. (Ⅰ) VCE-AIC reduces the ADC sampling frequency and optimizes the circuit structure. VCE-AIC uses multi-channel measurement to compress the overall sampling frequency to 48% and optimizes the actual number of parallel measurement channels to 12. VCE-AIC significantly improves the circuit implementability with an easy-to-implement three-value observation matrix. (Ⅱ) VCE-AIC can compress the data volume up to 14%, which greatly reduces the data communication volume of PD monitoring. (Ⅲ) VCE-AIC effectively solves the circuit and performance conflicts faced by online monitoring of PD signals due to its multi-channel compressed sampling and circuit structure optimization. In addition, it also has data security and limited denoising capability. The compressed sampling method in this paper reduces the component parameter requirements and circuit structure complexity of the sensing equipment and meets the demand for online monitoring of PD signals of power equipment.

Key words： Partial discharge compressed sensing measurement matrix incoherence virtual channel

收稿日期: 2022-03-14

PACS:

TM935

通讯作者: 赵洪山男,1965年生,教授,博士生导师,研究方向为电力设备的在线监测与态势感知、风电机组的故障预测与优化检修等。E-mail：zhaohshcn@126.com

作者简介: 赵仕策男,1994 年生,博士研究生,研究方向为配电设备的多参量传感与智能感知方法。E-mail：zscsysut@163.com

引用本文:

赵仕策, 赵洪山, 曲岳晗, 马利波, 任惠. 基于虚拟通道延拓的局部放电信号压缩采样方法[J]. 电工技术学报, 0, (): 156-156. Zhao Shice, Zhao Hongshan, Qu Yuehan, Ma Libo, Ren Hui. Research on Compressed Sampling of Partial Discharge Signal based on Virtual Channel Extension. Transactions of China Electrotechnical Society, 0, (): 156-156.

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[1] 马鑫, 尚毅梓, 胡昊, 等. 基于数据特征增强和残差收缩网络的变压器故障识别方法[J]. 电力系统自动化, 2022, 46(3): 175-183.
Ma Xin, Shang Yizi, Hu Hao, et al.Identification method of transformer fault based on data feature enhancement and residual shrinkage network[J]. Automation of Electric Power Systems, 2022, 46(3): 175-183.
[2] 周利军, 刘聪, 权圣威, 等. 基于点对称变换的乙丙橡胶电缆终端缺陷诊断[J]. 电工技术学报, 2022, 37(9): 2388-2398.
Zhou Lijun, Liu Cong, Quan Shengwei, et al.Defect diagnosis of EPR cable terminal based on symmetrized dot pattern[J]. Transactions of China Electrotechnical Society, 2022, 37(9): 2388-2398.
[3] Ghosh R, Chatterjee B, Dalai S.A method for the localization of partial discharge sources using partial discharge pulse information from acoustic emissions[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2017, 24(1): 237-245.
[4] 单秉亮, 李舒宁, 杨霄, 等. XLPE 配电电缆缺陷诊断与定位技术面临的关键问题[J]. 电工技术学报,2021, 36(22): 4809-4819.
Shan Bingliang, Li Shuning, Yang Xiao, et al.Key problems faced by defect diagnosis and location technologies for XLPE distribution cables[J]. Transactions of China Electrotechnical Society, 2021, 36(22): 4809-4819.
[5] Deng Rui, He Jing, Yu Jianjun, et al.Increasing data rate of an optical IMDD system using a cost-efficient dual-band transmission scheme based on RTZ DAC and sub-Nyquist sampling ADC[J]. Optics Express, 2018, 26(9): 11599-11607.
[6] 卿晓东, 苏玉刚. 电场耦合无线电能传输技术综述[J]. 电工技术学报, 2021, 36(17): 3649-3663.
Qing Xiaodong, Su Yugang.An overview of electricfiledcoupling wireless power transfer technology[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3649-3663.
[7] 王佩月, 左志平, 孙跃, 等. 基于双侧LCC 的全双工无线电能传输能量信号并行传输系统[J]. 电工技术学报, 2021, 36(23): 4981-4991.
Wang Peiyue, Zuo Zhiping, Sun Yue, et al.Fullduplex simultaneous wireless power and data transfer system based on double-sided LCC topology[J]. Transactions of China Electrotechnical Society, 2021, 36(23): 4981-4991.
[8] Song Shoupeng, Yu Jiahui, Shen Jingjing.Novel circuit implementation method for pulse signal finite rate of innovation sparse sampling based on an improved exponential reproducing kernel[J]. Circuits, Systems, and Signal Processing, 2019, 38(10): 4683-4699.
[9] Lissner I, Urban P.Toward a unified color space for perception-based image processing[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 2012, 21(3): 1153-1168.
[10] 于华楠, 武云瑞, 胡绪超. 基于压缩感知的电力设备视频图像去噪方法研究[J]. 电测与仪表, 2016, 53(18): 10-13, 40.
Yu Huanan, Wu Yunrui, Hu Xuchao.The research of power equipment image de-noising algorithm based on compressed sensing[J]. Electrical Measurement & Instrumentation, 2016, 53(18): 10-13, 40.
[11] Shen Zhiyuan, Wang Qianqian.A high-precision spectrum-detection algorithm based on the normalized variance of nonreconstruction compression sensing[J]. Mathematical Problems in Engineering, 2020, 2020: 1-9.
[12] Wu Feiyun, Yang Kunde, Duan Rui, et al.Compressive sampling and reconstruction of acoustic signal in underwater wireless sensor networks[J]. IEEE Sensors Journal, 2018, 18(14): 5876-5884.
[13] Chen F, Chandrakasan A P, Stojanovic V M.Design and analysis of a hardware-efficient compressed sensing architecture for data compression in wireless sensors[J]. IEEE Journal of Solid-State Circuits, 2012, 47(3): 744-756.
[14] Majidi M, Fadali M S, Etezadi-Amoli M, et al.Partial discharge pattern recognition via sparse representation and ANN[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2015, 22(2): 1061-1070.
[15] 谢军, 刘云鹏, 刘磊, 等. 局放信号自适应加权分帧快速稀疏表示去噪方法[J]. 中国电机工程学报,2019, 39(21): 6428-6439.
Xie Jun, Liu Yunpeng, Liu Lei, et al.A partial discharge signal denoising method based on adaptive weighted framing fast sparse representation[J]. Proceedings of the CSEE, 2019, 39(21): 6428-6439.
[16] Donoho D L.Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[17] Mangia M, Pareschi F, Rovatti R, et al.Adapted compressed sensing: a game worth playing[J]. IEEE Circuits and Systems Magazine, 2020, 20(1): 40-60.
[18] Haque T, Yazicigil R T, Pan K J L, et al. Theory and design of a quadrature analog-to-information converter for energy-efficient wideband spectrum sensing[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2015, 62(2): 527-535.
[19] Rani M, Dhok S B, Deshmukh R B.A systematic review of compressive sensing: concepts, implementations and applications[J]. IEEE Access, 2018, 6: 4875-4894.
[20] Kirolos S, Laska J, Wakin M, et al.Analog-toinformation conversion via random demodulation[C]//2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software, Richardson, TX, USA, 2007: 71-74.
[21] Mishali M, Eldar Y C.From theory to practice: subnyquist sampling of sparse wideband analog signals[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 375-391.
[22] Zhao Yijiu, Hu Yu hen, Wang Houjun. Enhanced random equivalent sampling based on compressed sensing[J]. IEEE Transactions on Instrumentation and Measurement, 2012, 61(3): 579-586.
[23] Zhao Yijiu, Hu Yu hen, Liu Jingjing. Random triggering-based sub-nyquist sampling system for sparse multiband signal[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(7): 1789-1797.
[24] 崔兴梅, 吴键, 徐云鹏. 基于改进m 序列的压缩采样观测矩阵设计[J]. 振动测试与诊断, 2017, 37(6):1163-1168, 1279.
Cui Xingmei, Wu Jian, Xu Yunpeng.Design of compressed sampling observation matrix based on improved m sequence[J]. Journal of Vibration, Measurement & Diagnosis, 2017, 37(6): 1163-1168, 1279.
[25] 赵仕策, 赵洪山, 寿佩瑶. 智能电力设备关键技术及运维探讨[J]. 电力系统自动化, 2020, 44(20): 1-10.
Zhao Shice, Zhao Hongshan, Shou Peiyao.Discussion on key technology and operation & maintenance of intelligent power equipment[J]. Automation of Electric Power Systems, 2020, 44(20): 1-10.
[26] 李庭, 陈杰, 于泓, 等. 压缩感知理论在IGBT 状态监测中的应用[J]. 电工技术学报, 2022, 37(增刊1):163-171.
Li Ting, Chen Jie, Yu Hong, et al.Application of compressed sensing method in condition monitoring of insulated gate bipolar transistor[J]. Transactions of China Electrotechnical Society, 2022, 37(S1): 163-171.
[27] Candès E, Romberg J.Sparsity and incoherence in compressive sampling[J]. Inverse Problems, 2007, 23(3): 969-985.
[28] Shen Zhi yuan, Cheng Xin miao, Wang Qian qian. A cooperative construction method for the measurement matrix and sensing dictionary used in compression sensing[J].EURASIP Journal on Advances in Signal Processing, 2020, 2020(1): 1-8.
[29] 王刘旺, 朱永利, 贾亚飞, 等. 局部放电大数据的并行PRPD 分析与模式识别[J]. 中国电机工程学报,2016, 36(5): 1236-1244.
Wang Liuwang, Zhu Yongli, Jia Yafei, et al.Parallel phase resolved partial discharge analysis for pattern recognition on massive PD data[J]. Proceedings of the CSEE, 2016, 36(5): 1236-1244.