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一种改进的基于正弦函数变步长LMS算法的谐波检测方法 被引量:3

An Improved Harmonic Detection Method Based on Sinusoidal Variable LMS Algorithm
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摘要 自适应谐波电流检测方法相对于其他谐波电流检测方法来说,具有实现简单、鲁棒性强等特点。针对有源电力滤波器(APF)的滤波特性,提出了一种改进的基于正弦函数变步长LMS算法的谐波电流检测方法。对于单纯抑制系统谐波电流以及同时兼顾抑制谐波与补偿无功这两种情况,该改进算法分别采取与传统方法不同的反馈信号作为自适应滤波器权系数迭代反馈量,并采用基于正弦函数的变步长LMS算法控制步长的变化,达到有效跟踪目标信号的目的。详细推导了两种情况下系统的直接误差信号,并将其作为正弦函数LMS算法的控制量来控制步长更新。通过MATLAB仿真对改进算法与传统固定步长算法进行比较,证明了改进算法的优越性。 Compared with other harmonic detection methods, the adaptive harmonic current detection method has the characteristics of simple implementation, strong robustness and so on.Aimed at the filtering characteristics of active power filter (APF), an improved harmonic current detection method based on sinusoidal variable step size LMS algorithm is proposed.For the cases of harmonic current suppression alone as well as harmonic suppression and reactive power compensation, the improved algorithm adopts feedback signals different from traditional methods as iterated feedback of weight coefficients of adaptive filters, and adopts variable step size LMS algorithm based on sinusoidal function to control the change of step size.The direct error signals of the system in two cases are deduced in detail in this paper and used as the control quantity of LMS algorithm with sinusoidal function to control step updating.The improved algorithm is compared with the traditional fixed step size algorithm by MATLAB simulation, which proves the superiority of the improved algorithm.
作者 王蕾 雷国赐 李珂 李金夫 周建 WANG Lei;LEI Guoci;LI Ke;Li Jinfu;ZHOU Jian(School of Information Engineering, Southwest University of Science and Technology,Mianyang 621010, Sichuan, China;The Information Communication of the National Power Grid Company of Mianyang Branch, Mianyang 621000, Sichuan, China)
出处 《西南科技大学学报》 CAS 2019年第2期77-82,共6页 Journal of Southwest University of Science and Technology
基金 四川省应用基础重点研发项目(2017GZ0346)
关键词 自适应算法 谐波检测 谐波抑制 变步长LMS算法 有源电力滤波器(APF) Adaptive algorithm Harmonic detection Harmonic suppression Variable step size LMS algorithm Active power filter
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