【摘要】：隨著電力電子技術(shù)的發(fā)展，以及大量非線性負(fù)荷在電力系統(tǒng)中的應(yīng)用，使得越來越多的諧波和間諧波存在于電網(wǎng)中。諧波污染影響著電力系統(tǒng)的安全運(yùn)行，同時使得電能質(zhì)量下降，因此對諧波問題的研究具有重要的意義。而諧波檢測作為諧波分析的出發(fā)點(diǎn)，高效、精確的諧波檢測方法研究具有一定的現(xiàn)實意義。 本文以電力系統(tǒng)諧波為研究對象，在詳細(xì)介紹了諧波產(chǎn)生的原因、造成的危害的基礎(chǔ)上，對國內(nèi)外諧波檢測算法進(jìn)行了對比研究，提出了基于蛙跳算法（Shuffled Frog LeapingAlgorithm,SFLA）的諧波檢測方法研究思路。 論文基于混沌映射，將混沌算子引入SFLA算法的全局搜索，利用全局最優(yōu)蛙引導(dǎo)進(jìn)化，提出了一種混沌蛙跳算法（Chaotic Shuffled Frog LeapingAlgorithm,CSFLA）,并融合最小二乘法（Least Square,LS）,提出一種基于CSFLA-LS的諧波檢測融合算法。討論了采樣頻率、數(shù)據(jù)窗長度以及直流分量對諧波檢測結(jié)果的影響，對諧波在存在噪聲的情況下進(jìn)行了仿真，結(jié)果表明了該融合算法的可行性與有效性。 分析了最新出現(xiàn)的分布估計算法，基于高斯分布估計的思想，在SFLA混編過程中，引入高斯分布建模的概念，從宏觀上對較優(yōu)秀的蛙進(jìn)行統(tǒng)計建模，從而提出一種基于高斯蛙跳算法（Gaussian Shuffled Frog Leaping Algorithm,GSFLA）的諧波檢測新算法，實驗仿真數(shù)據(jù)顯示，與PSO算法相比，振幅平均估計精度提高了5.3%，相角平均估計精度提高了4.7°。研究表明，該算法（GSFLA）用于電力系統(tǒng)的諧波檢測有更快的收斂速度和估計精度，檢測算法有效可行。 最后，對全文的研究工作進(jìn)行了總結(jié)，并針對論文研究工作的局限性，展望進(jìn)一步的研究工作。諧波的智能檢測將是諧波檢測方法的研究趨勢，，必將得到非常迅速的發(fā)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 擄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.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM711

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