基于改进LMS算法的谐波电流检测

李乾坤; 刘毅力; 刘圣荇

doi:10.16513/j.2096-2185.DE.2207502

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分布式能源 ›› 2022, Vol. 7 ›› Issue (5) : 9-16. DOI: 10.16513/j.2096-2185.DE.2207502

学术研究

基于改进LMS算法的谐波电流检测

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Harmonic Current Detection Based on Improved LMS Algorithm

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摘要

为了提升谐波电流的检测效果，使有源滤波器(active power filter，APF)能更好地消除谐波，首先在APF的基础上，采用固定步长最小均方算法(least mean square，LMS)的同时嵌入低通滤波器进行自适应谐波电流检测，然后采用麻雀搜索算法(sparrow search algorithm，SSA)对滤波的参数进行改进，接着通过改变负载参数值，来验证改进后的LMS算法在负载取不同值下同样适用，最后使用Matlab搭建仿真模型，并在模型中对比了固定步长LMS算法、SSA算法、粒子群算法(particle swarm optimization，PSO)这3种不同方法改进滤波参数的仿真效果，仿真结果表明使用SSA算法来优化参数效果最好，可使流经电网的电流达到只有基波电流的水平，并将电流的总谐波失真率降低到5%以下，满足IEEE标准，使电流的谐波含量降低，系统更加稳定，同时也表明了该方法的适用性和可行性。

Abstract

In order to improve the detection effect of harmonic current and enable the active filter (APF) to better eliminate harmonics, this paper firstly uses a fixed-step minimum least mean square (LMS) algorithm on the basis of APF and embeds a low-pass filter. Then use the sparrow search algorithm (SSA) to improve the filtering parameters, and then change the load parameter value to verify that the improved LMS algorithm is also applicable under different load values, and finally by using Matlab a simulation model was built, and the three different methods of fixed-step LMS algorithm, SSA algorithm, and particle swarm algorithm (PSO) were compared in the model to improve the simulation effect of filtering parameters. The simulation results show that the SSA algorithm is used to optimize the parameters. It can make the current flowing through the power grid reach the level of only the fundamental current, and reduce the total harmonic distortion (THD) of the current to less than 5%, which meets the IEEE standard, reduces the harmonic content of the current, and makes the system more stable. The applicability and feasibility of the method are also shown.

导出引用

李乾坤, 刘毅力, 刘圣荇. 基于改进LMS算法的谐波电流检测[J]. 分布式能源. 2022, 7(5): 9-16 https://doi.org/10.16513/j.2096-2185.DE.2207502

Qiankun LI, Yili LIU, Shengxing LIU. Harmonic Current Detection Based on Improved LMS Algorithm[J]. Distributed Energy Resources. 2022, 7(5): 9-16 https://doi.org/10.16513/j.2096-2185.DE.2207502

中图分类号： TK01; TM74

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[1]	杨立军，毛宇阳，杨志，等. 高性能谐波电流检测及控制方法[J]. 电力电子技术，2019, 53(8): 1-3. YANG Lijun, MAO Yuyang, YANG Zhi, et al. A high-performance harmonic current detection method and control strategy[J]. Power Electronics, 2019, 53(8): 1-3. 本文引用 [1]

[2]	张建忠，耿治，徐帅，等. 一种有源电力滤波器的改进自适应谐波检测算法[J]. 电工技术学报，2019, 34(20): 4323-4333. ZHANG Jianzhong, GENG Zhi, XU Shuai, et al. An improved adaptive harmonic detection algorithm for active power filter[J]. Transactions of China Electrotechnical Society, 2019, 34(20): 4323-4333. 本文引用 [1]

[3]	王清亮，应欣峰，宋曦，等. 基于i_p-i_q法的改进谐波电流检测方法[J]. 电子测量技术，2022, 45(6): 72-78. WANG Qingliang, YING Xinfeng, SONG Xi, et al. Improved harmonic current detection method based on i_p-i_q method[J]. Electronic Measurement Technology, 2022, 45(6): 72-78. 本文引用 [1]

[4]	李翔. 有源电力滤波器谐波检测与抑制方法研究[D]. 陕西：西安科技大学，2020. LI Xiang. Research on harmonic detection and suppression method of active power filter [D]. Shaanxi: Xi'an University of Science and Technology, 2020. 本文引用 [1]

[5]	彭咏龙，张坤锋，李亚斌，等. 基于自适应算法的谐波检测方法研究[J]. 电测与仪表，2018, 5(55): 6-9. PENG Yonglong, ZHANG Kunfeng, LI Yabin, et al. Research on harmonic detection method based on adaptive algorithm[J]. Electrical Measurement and Instrumentation, 2018, 5(55): 6-9. 本文引用 [1]

[6]	CHAUDHARI K R, TRIVEDI T A. Analysis on control strategy of shunt active power filter for three-phase three-wire system[C]//2014 IEEE PES Transmission & Distribution Conference and Exposition-Latin America (PES T&D-LA). 2014: 1-6. 本文引用 [1]

[7]	刘倩倩. 自适应滤波算法及其在电力谐波检测的应用[D]. 成都：西南交通大学，2018. LIU Qianqian. Adaptive filtering algorithm and its application in power harmonic detection [D]. Chengdu: Southwest Jiaotong University, 2018. 本文引用 [1]

[8]	SINGH B, SOLANKI J. An implementation of an adaptive control algorithm for a three-phase shunt active filter[J]. IEEE Transactions on Industrial Electronics, 2009, 56(8): 2811-2820. 本文引用 [1]

[9]	叶朋，张东洋，李广剑，等. 基于改进LMS算法的引信自适应去噪声研究[J]. 火力与指挥控制，2020, 45(10): 63-66. YE Peng, ZHANG Dongyang, LI Guangjian, et al. Adaptive noise cancellation in fuze based on the improved LMS algorithm[J]. Fire Control & Command Control, 2020, 45(10): 63-66. 本文引用 [1]

[10]	白群，赵闻蕾. 基于自适应滤波算法的牵引网谐波电流检测[J]. 大连交通大学学报，2021, 6(42): 111-115. BAI Qun, ZHAO Wenlei. An Improved variable step size LMS adaptive filtering algorithm[J]. Journal of Dalian Jiaotong University, 2021, 6(42): 111-115. 本文引用 [1]

[11]	张展，王维，史松林，等. 基于改进双曲正切函数的LMS谐波电流检测算法[J]. 武汉大学学报(工学版), 2020, 53(12): 1085-1090. ZHANG Zhan, WANG Wei, SHI Songlin, et al. LMS harmonic current detection algorithm based on improved hyperbolic tangent function[J]. Engineering Journal of Wuhan University, 2020, 53(12): 1085-1090. 本文引用 [1]

[12]	PERESADA S, ZAICHENKO Y, KOVBASA S, et al. Adaptive current control for shunt active power filters[C]//2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO), 2018: 598-604. 本文引用 [1]

[13]	ZHOU Y, SHAO W, CHENG Y, et al. An improved LMS harmonic current detection algorithm[C]//2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES), 2021: 50-54. 本文引用 [1]

[14]	王亚军. 基于变步长LMS算法的谐波电流检测与治理研究[D]. 大庆：东北石油大学，2021. WANG Yajun. Research on harmonic current detection and control based on variable step size LMS algorithm [D]. Daqing: Northeast Petroleum University, 2021. 本文引用 [1]

[15]	XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1), 22-34. 本文引用 [1]

[16]	邵文权，徐嘉鹏，卫晓辉，等. 采用PI＋重复控制策略的配电网有源电流消弧方案[J]. 西安工程大学学报，2022, 36(3): 1-8. SHAO Wenquan, XU Jiapeng, WEI Xiaohui, at al. A strategy active current arc suppression scheme using PI and repetitive control for distribution networks[J]. Journal of Xi'an Polytechnic University, 2022, 36(3): 1-8. 本文引用 [1]

[17]	李鹏，丁倩雯. 基于麻雀算法优化的OSTU分割算法[J]. 电子测量技术，2021, 44(19): 148-154. LI Peng, DING Qianwen. OSTU segmentation algorithm based on sparrow algorithm optimization[J]. Electronic Measurement Technology, 2021, 44(19): 148-154. 本文引用 [1]

[18]	戈一航，杨光永，徐天奇，等. 基于SSA优化PID在移动机器人路径跟踪中的研究[J]. 国外电子测量技术，2021, 40(09): 64-69. GE Yihang, YANG Guangyong, XU Tianqi, at al. Research on optimized PID based on SSA in path tracking of mobile robot[J]. Foreign Electronic Measurement Technology, 2021, 40(09): 64-69. 本文引用 [1]