本文關(guān)鍵詞：基于粒子群優(yōu)化自適應(yīng)最小二乘法的電網(wǎng)動(dòng)態(tài)諧波估計(jì)　出處：《深圳大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 電力系統(tǒng) 電能質(zhì)量 諧波估計(jì) 自適應(yīng)最小二乘法 粒子群優(yōu)化算法

【摘要】：電力技術(shù)的快速發(fā)展,越來越多的非線性電子元件和設(shè)備應(yīng)用到電力系統(tǒng)中,所產(chǎn)生的諧波問題成為主要的電能質(zhì)量問題。為了解決電網(wǎng)諧波問題,國(guó)內(nèi)外學(xué)者對(duì)此已經(jīng)做了許多深入的研究,并取得許多斐然的成果。本文對(duì)國(guó)內(nèi)外現(xiàn)有的電力系統(tǒng)的檢測(cè)方法如:有傅里葉變換(FT)、快速傅里葉變換(FFT)、小波變換(wavelet transform)、人工神經(jīng)網(wǎng)絡(luò)、卡爾曼濾波(Kalman filter,KF)等方法進(jìn)行了深入細(xì)致的研究,對(duì)它們的檢測(cè)性能和適用性進(jìn)行了探討。本文首先給出了幾種常用的電能質(zhì)量檢測(cè)方法,有效值法(root mean square,RMS)、傅里葉變換法、小波變換法、卡爾曼濾波算法和自適應(yīng)最小二乘法(recursive least square,RLS),對(duì)各種方法進(jìn)行了仿真分析并對(duì)各個(gè)檢測(cè)方法的優(yōu)缺點(diǎn)給出了分析和比較。然后,本文了研究基于粒子群優(yōu)化自適應(yīng)最小二乘法(particle swarm optimized recursive least square,PSO-RLS)的電網(wǎng)諧波估計(jì)方法。在研究和分析了RLS算法后,針對(duì)RLS算法的不足,本文提出利用粒子群(particle swarm optimization,PSO)算法求解自適應(yīng)最小二乘法(RLS)所需的最優(yōu)化的電網(wǎng)諧波參數(shù),即狀態(tài)向量的權(quán)重的初始值,在得到優(yōu)化后的初始權(quán)重參數(shù)后再利用RLS算法對(duì)電網(wǎng)諧波參數(shù)進(jìn)行參數(shù)估計(jì)。本文所提出的方法克服了自適應(yīng)最小二乘法(RLS)對(duì)初始參數(shù)敏感的問題,優(yōu)化了RLS算法的諧波估計(jì)效果。最后,本文應(yīng)用PSO-RLS算法對(duì)靜態(tài)和動(dòng)態(tài)的電壓信號(hào)進(jìn)行仿真分析,并比較了不同的噪聲環(huán)境下參數(shù)估計(jì)效果,此外,還應(yīng)用本文所提方法對(duì)電網(wǎng)動(dòng)態(tài)子諧波和間諧波進(jìn)行了仿真分析。仿真結(jié)果表明,與可變約束最小二乘方法(Variable Constraint based Least Mean Square,VCLMS),遺傳算法(Genetic Algorithm,GA)優(yōu)化參數(shù)估計(jì)相比,本文所提方法估計(jì)效果更優(yōu)。
[Abstract]:With the rapid development of power technology, more and more nonlinear electronic components and devices are applied to power system. The harmonic problem becomes the main power quality problem in order to solve the harmonic problem. Scholars at home and abroad have done a lot of in-depth research, and achieved a lot of remarkable results. In this paper, the existing detection methods of power systems at home and abroad, such as Fourier transform Fourier transform (FTFT). Fast Fourier transform (FFT), wavelet transform (WT), wavelet transform (WT), artificial neural network (Ann), Kalman filter (Kalman). KF) and other methods are studied in detail, and their detection performance and applicability are discussed. Firstly, several commonly used power quality detection methods are given in this paper. The effective value method is root mean square, Fourier transform and wavelet transform. Kalman filter algorithm and adaptive least square method recursive least squared RLS). All kinds of methods are simulated and analyzed, and the advantages and disadvantages of each detection method are analyzed and compared. In this paper, the particle swarm optimized recursive least square based on particle swarm optimization is studied. After studying and analyzing the RLS algorithm, the deficiency of the RLS algorithm is pointed out. In this paper, a particle swarm optimization is proposed. PSO) algorithm is used to solve the optimal harmonic parameters of the power network, that is, the initial value of the weight of the state vector, which is required by the adaptive least square method (RLSs). The RLS algorithm is used to estimate the harmonic parameters of the power network after the optimized initial weight parameters are obtained. The method proposed in this paper overcomes the adaptive least square method (RLS). Sensitive to initial parameters. The harmonic estimation effect of RLS algorithm is optimized. Finally, the static and dynamic voltage signals are simulated and analyzed by using PSO-RLS algorithm, and the effect of parameter estimation in different noise environment is compared. In addition, the dynamic subharmonics and interharmonics of the power network are simulated and analyzed by using the method proposed in this paper. The simulation results show that. And variable Constraint based Least Mean squared VCLMSs. Genetic algorithm (GA) is more effective than genetic algorithm in parameter estimation.
【學(xué)位授予單位】：深圳大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM714;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 蔣曉\~;任佳;顧敏明;;多維度慣性權(quán)重衰減混沌化粒子群算法及應(yīng)用[J];儀器儀表學(xué)報(bào);2015年06期

2 江輝;謝興;王志忠;彭建春;;基于優(yōu)化無跡Kalman濾波的電網(wǎng)動(dòng)態(tài)諧波估計(jì)[J];深圳大學(xué)學(xué)報(bào)(理工版);2015年02期

3 紀(jì)雪玲;李明;李瑋;;一種克服局部最優(yōu)的收縮因子PSO算法[J];計(jì)算機(jī)工程;2011年20期

4 高建輝;;LMS自適應(yīng)濾波器的設(shè)計(jì)理論研究[J];信息技術(shù);2011年08期

5 楊宇;施未來;;變步長(zhǎng)LMS自適應(yīng)濾波算法研究[J];江蘇教育學(xué)院學(xué)報(bào)(自然科學(xué)版);2011年01期

6 劉國(guó)海;呂漢聞;劉穎;陳兆嶺;劉慧;;基于改進(jìn)RLS算法的諧波電流檢測(cè)方法[J];電力自動(dòng)化設(shè)備;2010年10期

7 王效孟;周勇;劉繼承;林烽;;檢測(cè)電壓暫降特征量的有效值算法[J];低壓電器;2010年10期

8 耿妍;張端金;;自適應(yīng)濾波算法綜述[J];信息與電子工程;2008年04期

9 李鑫濱;朱慶軍;馬紅霞;李強(qiáng)波;;粒子群算法及其在電力系統(tǒng)無功優(yōu)化中的應(yīng)用綜述[J];燕山大學(xué)學(xué)報(bào);2008年03期

10 趙鳳展;楊仁剛;;基于短時(shí)傅里葉變換的電壓暫降擾動(dòng)檢測(cè)[J];中國(guó)電機(jī)工程學(xué)報(bào);2007年10期

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