| 引用本文: | 李裕杰,赵庆生,王旭平,郭尊.基于线性约束最小均方的谐波检测算法[J].电力系统保护与控制,2019,47(11):16-21.[点击复制] |
| LI Yujie,ZHAO Qingsheng,WANG Xuping,GUO Zun.A harmonic detection algorithm based on linearly constrained least mean square[J].Power System Protection and Control,2019,47(11):16-21[点击复制] |
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| 摘要: |
| 最小均方(Least Mean Square, LMS)算法因其计算复杂度低、稳定性好的特点已广泛应用于谐波检测领域中。但为了避免权重偏移，进一步提高收敛速度，提出了一种基于线性约束最小均方(Linearly Constrained Least Mean Square, LCLMS)的谐波检测算法。该算法在LMS算法的基础上，对权重变量加入了一个线性约束条件，并应用于不同高斯白噪声环境下谐波、间谐波信号的幅值和相角参数评估。最后又在稳态信号、动态信号和电弧炉算例下检验了该算法的可行性。实验结果表明，该算法可以快速准确地检测不同环境下谐波的相关信息，且相比LMS算法有较快的收敛速度和较高的抗干扰能力。 |
| 关键词: 最小均方 谐波检测 权重偏移 线性约束最小均方 线性约束 |
| DOI：10.7667/PSPC20191103 |
| 投稿时间：2018-07-12修订日期：2018-09-19 |
| 基金项目:国家自然科学基金青年基金项目资助(51505317);山西省自然科学基金项目资助(201601D102039) |
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| A harmonic detection algorithm based on linearly constrained least mean square |
| LI Yujie,ZHAO Qingsheng,WANG Xuping,GUO Zun |
| (Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan 030024, China;School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China) |
| Abstract: |
| In order to avoid weight offset and improve the convergence speed, this paper presents the harmonic detection algorithm based on linearly constrained least mean square, although the least mean square algorithm has been widely used in the field of harmonic detection because of its low computational complexity and good stability. New algorithm adds a linear constraint condition of the weight variable. The amplitude and phase of a power signal containing harmonics and inter-harmonics are estimated using this algorithm in the presence of white Gaussian noise under simulating environment. Finally, the algorithm is tested under the steady-state signal, the dynamic signal and the arc furnace model. According to experimental results, this algorithm can detect the information of harmonics quickly and accurately in different environments, and it has faster convergence speed and higher anti-interference ability compared with the LMS algorithm. This work is supported by National Natural Science Foundation for Young Scholars (No. 51505317) and Natural Science Foundation of Shanxi Province (No. 201601D102039). |
| Key words: least mean square harmonic detection weight offset linearly constrained least mean square linear constraint |
