School of Electrical Engineering, Yanshan University, Qinhuangdao City, Hebei Province 066000, China.
Rev Sci Instrum. 2021 Feb 1;92(2):025118. doi: 10.1063/1.5141923.
In order to accurately extract harmonic components in power signals, a harmonic detection method based on variational mode decomposition (VMD) optimized by the genetic algorithm and permutation entropy is proposed. The genetic algorithm optimizes VMD, which can improve the difficulty of parameter selection of VMD. The combination of optimized VMD and permutation entropy can simultaneously utilize the complete decomposition of VMD and the sensitivity to the time of permutation entropy and quickly and effectively filter out various interference signals of power systems. The simulation experiment and engineering application prove that compared with empirical mode decomposition and ensemble empirical mode decomposition harmonic detection methods, this method can effectively filter out noise under a low signal-to-noise ratio and achieve a higher harmonic detection accuracy.
为了准确提取电力信号中的谐波分量，提出了一种基于遗传算法和排列熵优化变分模态分解（VMD）的谐波检测方法。遗传算法优化 VMD，可以提高 VMD 参数选择的难度。优化 VMD 与排列熵的组合可以同时利用 VMD 的完全分解和排列熵对时间的敏感性，快速有效地滤除电力系统的各种干扰信号。仿真实验和工程应用证明，与经验模态分解和集合经验模态分解的谐波检测方法相比，该方法可以在低信噪比下有效滤除噪声，达到更高的谐波检测精度。
