遺傳算法在電力系統(tǒng)無(wú)功優(yōu)化中的應(yīng)用研究

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目 錄
摘要 II
Abstract III
第一章 緒論 1
1.1遺傳算法的背景和意義 1
1.1.1遺傳算法的發(fā)展歷史 2
1.1.2遺傳算法的研究現(xiàn)狀以及特點(diǎn) 3
1.2電力系統(tǒng)無(wú)功優(yōu)化的背景和意義 4
1.2.1電力系統(tǒng)無(wú)功優(yōu)化的研究現(xiàn)狀 5
第二章 遺傳算法 8
2.1遺傳算法基本操作 8
2.2 遺傳算法的基本定理 9
2.3 遺傳算法的解題步驟 12
第三章 遺傳算法在無(wú)功優(yōu)化中的應(yīng)用 15
3.1 無(wú)功優(yōu)化規(guī)劃模型描述 15
3.2 無(wú)功優(yōu)化的模型求解過(guò)程 18
3.3計(jì)算實(shí)例 20
3.4模型求解——遺傳算法及改進(jìn) 22
第四章 結(jié)論與展望 24
4.1結(jié)論 24
4.2展望 25
4.2.1 遺傳算法的發(fā)展趨勢(shì) 25
4.2.2電力系統(tǒng)無(wú)功優(yōu)化的發(fā)展趨勢(shì) 26
致謝 28
參考文獻(xiàn) 29

摘要
進(jìn)入二十一世紀(jì)以來(lái),我國(guó)的電力工業(yè)迅速發(fā)展,電力用戶(hù)對(duì)電能質(zhì)量的要求越來(lái)越高,如何保證現(xiàn)代電力系統(tǒng)的安全、穩(wěn)定、經(jīng)濟(jì)運(yùn)行成為當(dāng)代電力工作者面臨的一個(gè)重要問(wèn)題。電力系統(tǒng)無(wú)功優(yōu)化能有效地降低電力系統(tǒng)的有功功率損耗、改善電網(wǎng)的電壓質(zhì)量,是保證電力系統(tǒng)安全、穩(wěn)定、經(jīng)濟(jì)運(yùn)行的重要手段。因此,對(duì)電力系統(tǒng)無(wú)功優(yōu)化問(wèn)題的研究,具有重要的理論指導(dǎo)意義和較高的實(shí)際應(yīng)用價(jià)值。電力系統(tǒng)無(wú)功優(yōu)化是一個(gè)既含有連續(xù)變量又含有離散變量的復(fù)雜的非線性規(guī)劃問(wèn)題,其求解過(guò)程異常繁瑣。傳統(tǒng)的無(wú)功優(yōu)化算法依賴(lài)于精確的數(shù)學(xué)模型,一般要求目標(biāo)函數(shù)連續(xù)、可導(dǎo),且不能精確的處理離散變量,致使在求解含有大量離散變量的電力系統(tǒng)無(wú)功優(yōu)化問(wèn)題時(shí)產(chǎn)生較大誤差,影響了計(jì)算結(jié)果的準(zhǔn)確性。而人工智能優(yōu)化算法不需要精確的數(shù)學(xué)模型,就能夠很好地處理非線性及離散性問(wèn)題,因此在優(yōu)化運(yùn)算中得到了廣泛的應(yīng)用。
本文在綜合分析當(dāng)前各種傳統(tǒng)優(yōu)化算法和人工智能算法優(yōu)缺點(diǎn)的基礎(chǔ)上,結(jié)合電力系統(tǒng)的實(shí)際,選取遺傳算法作為求解電力系統(tǒng)無(wú)功優(yōu)化問(wèn)題的方法。針對(duì)電力系統(tǒng)無(wú)功優(yōu)化的特點(diǎn),選取電力系統(tǒng)有功網(wǎng)損最小為目標(biāo)函數(shù),運(yùn)用計(jì)算速度和精度較高的快速解耦法進(jìn)行潮流計(jì)算。此外,文中還詳細(xì)介紹了遺傳算法的基本原理及電力系統(tǒng)無(wú)功優(yōu)化的目的和意義。
關(guān)鍵詞：遺傳算法 電力系統(tǒng) 無(wú)功優(yōu)化 潮流計(jì)算


Abstract
The twenty-first century, the rapid development of China's power industry , power quality power users have become increasingly demanding , how to ensure the security of modern power systems , stability and economic operation of power has become an important contemporary issues facing workers . Reactive power optimization of power system can effectively reduce the active power loss of the power system , to improve the quality of the grid voltage , the power system is an important means to ensure security, stability and economic operation. Therefore, reactive power optimization problem , has important theoretical significance and high practical value. Reactive power optimization is a complex nonlinear programming problem containing both a continuous variable and discrete variables , the solution process anomalies cumbersome. Traditional reactive power optimization algorithm relies on accurate mathematical model , generally require continuous objective function can lead , and can not accurately handling discrete variables , resulting in solving power system contains a large number of discrete variables produce large errors when the reactive power optimization problem, affect the accuracy of the calculated results . The artificial intelligence optimization algorithm does not require a precise mathematical model , we can deal with nonlinear and discrete problems well , so it has been widely used in the optimization calculation . In this paper, a comprehensive analysis of the current basis of a variety of traditional optimization algorithms and artificial intelligence algorithms advantages and disadvantages, with the actual power system , select the genetic algorithm as a method for solving reactive power optimization problem . For reactive power optimization features , the paper selected power system active power loss minimization objective function , using a penalty function to deal with the constraints of power system state variables , flow calculation using the computing speed and high precision fast decoupled method. In addition, the paper also introduces the basic principles of genetic algorithms and various improvements as well as the purpose and significance of reactive power optimization .
Key words： Genetic Algorithms; Reactive Power Optimization; Flow Calculation