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    Suzhou Electric Appliance Research Institute
    期刊號: CN32-1800/TM| ISSN1007-3175

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    UPFC抑制電力系統(tǒng)低頻振蕩的參數(shù)優(yōu)化研究

    來源:電工電氣發(fā)布時間:2016-12-19 09:19 瀏覽次數(shù):8
    UPFC抑制電力系統(tǒng)低頻振蕩的參數(shù)優(yōu)化研究
     
    肖佳宜,李梓萌,申夢茜,王文文
    (山東科技大學(xué) 電氣與自動化工程學(xué)院,山東 青島 266590)
     
        摘 要:為研究統(tǒng)一潮流控制器(UPFC) 用于抑制電力系統(tǒng)低頻振蕩的效果,采用PID 控制策略設(shè)計了UPFC,并利用遺傳算法(GA) 和粒子群算法(PSO) 分別對UPFC 參數(shù)進行了優(yōu)化。對含UPFC 的單機無窮大系統(tǒng)進行仿真,仿真結(jié)果顯示,UPFC 對阻尼電力系統(tǒng)低頻振蕩起到一定的作用,經(jīng)過參數(shù)優(yōu)化后系統(tǒng)的暫態(tài)變短,相關(guān)量的波動降低,且粒子群優(yōu)化算法用于抑制阻尼電力系統(tǒng)低頻振蕩的效果優(yōu)于遺傳算法。
        關(guān)鍵詞:統(tǒng)一潮流控制器;電力系統(tǒng)低頻振蕩;遺傳算法;粒子群算法;單機無窮大系統(tǒng)
        中圖分類號:TM712     文獻標識碼:A     文章編號:1007-3175(2016)12-0031-05
     
    Research on Unified Power Flow Controller Parameter Optimization of
    Low Frequency Oscillation Suppression in Power System
     
    XIAO Jia-yi, LI Zi-meng, SHEN Meng-xi, WANG Wen-wen
    (College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China)
     
        Abstract: In order to research on the effect of unified power flow controller (UPFC) used for suppressing the low frequency oscillation of electrical power system, this paper adopted the proportion integration differentiation (PID) control strategy to design the UPFC. The genetic algorithm (GA) and the particle swarm optimization(PSO) were used to optimize the parameters of UPFC respectively. The single-machine infinite bus system with UPFC was carried out simulation. The simulation results show that the UPFC plays a role in the damping low frequency oscillation of electrical power system. The system transient state of parameter optimization gets shorter, the fluctuation of correlation quantity gets lower and the effect of particle swarm optimization used for the damping low frequency oscillation of electrical power system is superior to that of GA.
        Key words: unified power flow controller; power system low frequency oscillation; genetic algorithm; particle swarm optimization; single-machine infinite bus system
     
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