本文選題：電力系統(tǒng) + 無功優(yōu)化　； 參考：《吉林大學(xué)》2014年碩士論文

【摘要】：在電力網(wǎng)絡(luò)中，實(shí)現(xiàn)無功電源的分布，和無功補(bǔ)償容量的設(shè)置，是一個帶有大量約束條件的非線性規(guī)劃問題，對于對大范圍供電的供電企業(yè)甚至全國范圍內(nèi)的電力能源調(diào)配而言，是個非常非常繁雜的過程。遺傳算法以初始種群為起點(diǎn)，沿多條路線進(jìn)行搜索，具有較強(qiáng)的尋優(yōu)能力，適合非線性離散、多約束、多變量、大規(guī)律問題的求解，可以較好的避免“維數(shù)災(zāi)”的問題，因此在電力系統(tǒng)無功優(yōu)化領(lǐng)域得到了廣泛的應(yīng)用。但是如果要獲得較好的解，那么遺傳算法的效率較低，而且也容易產(chǎn)生早熟現(xiàn)象。 為此，本文主要針對目前電力系統(tǒng)無功優(yōu)化的研究現(xiàn)狀，針對電力系統(tǒng)無功優(yōu)化的特點(diǎn)，通過對簡單遺傳算法的改進(jìn)來實(shí)現(xiàn)對電力系統(tǒng)的無功優(yōu)化問題進(jìn)行研究，本文主要的研究內(nèi)容如下： 首先，針對電力系統(tǒng)的無功優(yōu)化問題，建立以電力系統(tǒng)中，電能損耗最小作為電力系統(tǒng)無功優(yōu)化問題的目標(biāo)函數(shù)，并且發(fā)電機(jī)無功越限、節(jié)點(diǎn)電壓越限作為問題的懲罰函數(shù)來進(jìn)行電力系統(tǒng)無功優(yōu)化數(shù)學(xué)模型的研究。 其次，針對電力系統(tǒng)無功優(yōu)化的特點(diǎn)，進(jìn)行遺傳算法的改進(jìn)，并且對改進(jìn)遺傳算法中的染色體編碼算法，選擇、變異、交叉等遺傳算子，適應(yīng)度函數(shù)的設(shè)計(jì)以及終止條件的確定等方面，對改進(jìn)遺傳算法的設(shè)計(jì)進(jìn)行研究。 最后，以一個具體的IEEE 14節(jié)點(diǎn)系統(tǒng)為例，利用本文研究的改進(jìn)的遺傳算法，和基本遺傳算法，對該電力系統(tǒng)中的無功優(yōu)化問題進(jìn)行求解，并且通過兩者的對比，從最終的電壓控制水平，和降損量兩個方面對本文所研究的改進(jìn)遺傳算法的效果進(jìn)行分析和驗(yàn)證。 根據(jù)電力系統(tǒng)無功優(yōu)化的特點(diǎn)，，對簡單遺傳算法中的編碼方式、交叉算子和變異算子的，以及遺傳算法的迭代終止條件進(jìn)行改進(jìn)，并且在一個具體的IEEE 14節(jié)點(diǎn)的無功優(yōu)化應(yīng)用中，表明本文所研究的改進(jìn)遺傳算法具有更高的性能和較低的電力系統(tǒng)有功損耗。
[Abstract]:In power network, the distribution of reactive power supply and the setting of reactive power compensation capacity is a nonlinear programming problem with a large number of constraints.It is a very, very complicated process for the distribution of power energy to the power supply enterprises and even the whole country.The genetic algorithm takes the initial population as the starting point and searches along several routes. It has a strong ability to search for optimization. It is suitable for solving nonlinear discrete, multi-constrained, multi-variable and large law problems, which can avoid the problem of "dimension disaster".Therefore, it has been widely used in the field of reactive power optimization in power system.But if we want to get a better solution, the efficiency of genetic algorithm is low, and premature phenomenon is easy to occur.For this reason, this paper mainly aims at the current research situation of reactive power optimization in power system, according to the characteristics of reactive power optimization in power system, through the improvement of simple genetic algorithm to realize the reactive power optimization problem of power system.The main contents of this paper are as follows:Firstly, aiming at the reactive power optimization problem in power system, the minimum power loss is established as the objective function of the reactive power optimization problem in power system, and the reactive power of generator exceeds the limit.As the penalty function of the problem, the node voltage limit is used to study the mathematical model of reactive power optimization in power system.Secondly, according to the characteristics of reactive power optimization in power system, genetic algorithm is improved, and genetic operators, such as chromosome coding algorithm, selection, mutation, crossover and so on, are improved in genetic algorithm.Based on the design of fitness function and the determination of termination conditions, the design of improved genetic algorithm is studied.Finally, taking a specific IEEE 14-bus system as an example, using the improved genetic algorithm and the basic genetic algorithm studied in this paper, the reactive power optimization problem in the power system is solved, and the comparison between the two is given.The effect of the improved genetic algorithm studied in this paper is analyzed and verified from the two aspects of the final voltage control level and the loss reduction.According to the characteristics of reactive power optimization in power system, the coding method, crossover operator, mutation operator and iterative termination condition of genetic algorithm in simple genetic algorithm are improved.And in a specific IEEE 14-bus reactive power optimization application, it is shown that the improved genetic algorithm studied in this paper has higher performance and lower active power loss in power system.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM714.3;TP18

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