基于改进蛙跳算法的谐波检测方法研究

发布时间：2018-08-14 19:15

【摘要】：随着电力电子技术的发展，以及大量非线性负荷在电力系统中的应用，使得越来越多的谐波和间谐波存在于电网中。谐波污染影响着电力系统的安全运行，同时使得电能质量下降，因此对谐波问题的研究具有重要的意义。而谐波检测作为谐波分析的出发点，高效、精确的谐波检测方法研究具有一定的现实意义。本文以电力系统谐波为研究对象，在详细介绍了谐波产生的原因、造成的危害的基础上，对国内外谐波检测算法进行了对比研究，提出了基于蛙跳算法（Shuffled Frog LeapingAlgorithm,SFLA）的谐波检测方法研究思路。论文基于混沌映射，将混沌算子引入SFLA算法的全局搜索，利用全局最优蛙引导进化，提出了一种混沌蛙跳算法（Chaotic Shuffled Frog LeapingAlgorithm,CSFLA）,并融合最小二乘法（Least Square,LS）,提出一种基于CSFLA-LS的谐波检测融合算法。讨论了采样频率、数据窗长度以及直流分量对谐波检测结果的影响，对谐波在存在噪声的情况下进行了仿真，结果表明了该融合算法的可行性与有效性。分析了最新出现的分布估计算法，基于高斯分布估计的思想，在SFLA混编过程中，引入高斯分布建模的概念，从宏观上对较优秀的蛙进行统计建模，从而提出一种基于高斯蛙跳算法（Gaussian Shuffled Frog Leaping Algorithm,GSFLA）的谐波检测新算法，实验仿真数据显示，与PSO算法相比，振幅平均估计精度提高了5.3%，相角平均估计精度提高了4.7°。研究表明，该算法（GSFLA）用于电力系统的谐波检测有更快的收敛速度和估计精度，检测算法有效可行。最后，对全文的研究工作进行了总结，并针对论文研究工作的局限性，展望进一步的研究工作。谐波的智能检测将是谐波检测方法的研究趋势，，必将得到非常迅速的发展。
[Abstract]:With the development of power electronics technology and the application of a large number of nonlinear loads in the power system, more and more harmonics and interharmonics exist in the power network. Harmonic pollution affects the safe operation of power system and reduces the power quality, so it is of great significance to study the harmonic problem. Harmonic detection as the starting point of harmonic analysis, efficient and accurate harmonic detection method has certain practical significance. This paper takes harmonic of power system as the research object, on the basis of introducing the cause and harm of harmonic generation in detail, the harmonic detection algorithms at home and abroad are compared and studied. A method of harmonic detection based on (Shuffled Frog Leap algorithm (SFLA) is proposed. Based on chaotic mapping, chaotic operator is introduced into global search of SFLA algorithm, and global optimal frog is used to guide evolution. A chaotic leaping algorithm (Chaotic Shuffled Frog Leap algorithm (CSFLA) is proposed, and the Least Square-LS algorithm is fused. A harmonic detection fusion algorithm based on CSFLA-LS is proposed. The effects of sampling frequency, data window length and DC component on harmonic detection results are discussed. The simulation results show that the proposed fusion algorithm is feasible and effective in the presence of noise. Based on the idea of Gao Si distribution estimation, the concept of Gao Si distribution modeling is introduced in the process of SFLA blending, and the statistical modeling of the better frog is carried out from the macro view. A new harmonic detection algorithm based on Gao Si leapfrog algorithm (Gaussian Shuffled Frog Leaping algorithm is proposed. The experimental results show that compared with PSO algorithm, the accuracy of amplitude average estimation and phase angle average estimation are increased by 5.3 and 4.7 掳respectively. The research shows that the algorithm (GSFLA) has faster convergence speed and estimation accuracy for harmonic detection in power system, and the algorithm is effective and feasible. Finally, the research work of this paper is summarized, and the further research work is prospected in view of the limitations of the research work. The intelligent detection of harmonics will be the research trend of harmonic detection methods and will be developed rapidly.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM711

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