本文選題：無功優(yōu)化　切入點：遺傳算法　出處：《蘭州交通大學》2014年碩士論文　論文類型：學位論文

【摘要】：隨著我國經(jīng)濟的迅速增長和工業(yè)的發(fā)展，國民經(jīng)濟各部門對電能質(zhì)量的要求也越來越嚴格。在電力系統(tǒng)中，無功功率起著特殊的作用。一方面，無功功率為電能的交換、輸送和轉(zhuǎn)換創(chuàng)造必要的條件；另一方面，如果無功電源和負荷分布不合理，將會影響電力系統(tǒng)的經(jīng)濟性和穩(wěn)定性，降低電能質(zhì)量。因而研究電力系統(tǒng)無功優(yōu)化，對減少電力網(wǎng)絡因為無功的不合理分配而產(chǎn)生的額外有功消耗和提升電壓運行水平具有顯著的現(xiàn)實意義。 電力系統(tǒng)的無功優(yōu)化本質(zhì)是一個最優(yōu)化問題，它的變量種類多，目標往往不止一個，數(shù)學模型復雜，處理規(guī)模大，對算法的實時性要求也高。尤其近年來電力系統(tǒng)規(guī)模越來越龐大，技術也越來越復雜。由于傳統(tǒng)算法的內(nèi)在局限性，已經(jīng)不能很好適應現(xiàn)代大規(guī)模的電力系統(tǒng)。近年來，人工智能算法開始應用于電力系統(tǒng)無功優(yōu)化領域，其中遺傳算法與其他智能算法比較起來應用更為廣泛。論文在現(xiàn)有研究基礎上對遺傳算法進行改進，以期進一步提高其求解的速度與精度。 論文首先介紹電力系統(tǒng)無功優(yōu)化的背景及意義，對研究內(nèi)容和特點進行分析，提出電力系統(tǒng)無功優(yōu)化模型，該模型綜合考慮了網(wǎng)損最小和維持電力系統(tǒng)的穩(wěn)定性。其次，提出ICGA(Improved Catastrophic Genetic Algorithm,改進災變遺傳算法)應用于電力系統(tǒng)無功優(yōu)化，該算法在常規(guī)遺傳算法的基礎上，引入災變算子，，并對產(chǎn)生災變的范圍進行動態(tài)控制，解決常規(guī)遺傳算法的易陷入局部最優(yōu)問題，同時提升收斂速度。此外，為進一步提高算法的收斂性能，論文還設計動態(tài)的交叉概率和變異概率。最后，采用測試函數(shù)驗證論文提出的算法的效果。 論文將ICGA與潮流計算結(jié)合運用到電力系統(tǒng)無功優(yōu)化中，并通過IEEE(Institude ofElectrical and Electronics Engineers,電氣和電子工程師協(xié)會)推薦的兩個標準節(jié)點進行仿真以驗證本算法的效果。結(jié)果表明，ICGA在保持群體多樣性和提高搜索效率等方面都具有良好的性能，對電力系統(tǒng)無功優(yōu)化能產(chǎn)生較理想的效果。
[Abstract]:With the rapid growth of economy and the development of industry in our country, the requirements of power quality are becoming more and more strict in all sectors of the national economy. Reactive power plays a special role in the power system. On the one hand, reactive power is the exchange of electric energy. On the other hand, if the distribution of reactive power and load is not reasonable, it will affect the economy and stability of power system and reduce the power quality. It is of great practical significance to reduce the extra active power consumption caused by the unreasonable distribution of reactive power and to raise the level of voltage operation in power network. The essence of reactive power optimization in power system is an optimization problem. It has many kinds of variables, more than one target, complex mathematical model and large scale of processing. Especially in recent years, the scale of power system is getting larger and larger, the technology is more and more complex. Because of the inherent limitation of traditional algorithm, it can not adapt to modern large-scale power system. Artificial intelligence algorithm has been applied to reactive power optimization of power system, and genetic algorithm is more widely used than other intelligent algorithms. In order to further improve the speed and accuracy of its solution. This paper first introduces the background and significance of reactive power optimization in power system, analyzes the contents and characteristics of the research, and puts forward a reactive power optimization model of power system, which considers the minimum network loss and maintains the stability of power system. ICGA(Improved Catastrophic Genetic algorithm (improved catastrophe genetic algorithm) is applied to reactive power optimization of power system. Based on the conventional genetic algorithm, the catastrophe operator is introduced and the range of catastrophe is dynamically controlled. In order to improve the convergence performance of the conventional genetic algorithm, the dynamic crossover probability and mutation probability are designed. Test function is used to verify the effect of the proposed algorithm. In this paper, ICGA and power flow calculation are applied to reactive power optimization of power system. Two standard nodes recommended by IEEE(Institude ofElectrical and Electronics Engineers are simulated to verify the effectiveness of this algorithm. The results show that ICGA has good performance in maintaining population diversity and improving search efficiency. The optimal reactive power optimization of power system can produce ideal effect.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM714.3

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