本文選題：電力系統(tǒng) + 無功優(yōu)化��； 參考：《上海電機學院》2017年碩士論文

【摘要】：電力系統(tǒng)的優(yōu)化和調(diào)整是可以促進電力系統(tǒng)經(jīng)濟和順利的發(fā)展,利用無功優(yōu)化能夠有效地保證電力系統(tǒng)的安全性,在提升供電質(zhì)量和增加經(jīng)濟收益的過程中發(fā)揮著非常重要的作用。通過電力系統(tǒng)的無功優(yōu)化應當能夠取得使有功損耗得到降低、電壓質(zhì)量得到提升以及使電力系統(tǒng)穩(wěn)定性得到增加的效果。粒子群算法在求解電力系統(tǒng)優(yōu)化問題上取得了很大的成果,但粒子群算法易陷入局部最優(yōu)的缺點限定了其現(xiàn)實的應用,本課題研究提出一種新的電力系統(tǒng)的優(yōu)化算法,即填充函數(shù)粒子群算法(FPSO),而結(jié)合填充函數(shù)的粒子群算法能有效克服粒子群算法的缺點,達到全局優(yōu)化的目的,更有成效地求解優(yōu)化問題。本課題首先介紹了無功優(yōu)化研究的背景以及對其所涉及算法的國內(nèi)外研究的現(xiàn)狀進行了總結(jié),然后以罰函數(shù)為基礎提出了無功優(yōu)化目標函數(shù)的數(shù)學模型及對其優(yōu)化的混合算法中粒子群算法和填充函數(shù)法的介紹,接著采用MATLAB工具對無功優(yōu)化進行研究仿真運算,以IEEE14節(jié)點系統(tǒng)為例進行有力的驗證,同時和標準的粒子群算法的優(yōu)化結(jié)果進行對比分析。得到的仿真結(jié)果可以證明,填充函數(shù)粒子群算法的混合方法在控制節(jié)點電壓和減少系統(tǒng)網(wǎng)損的兩個方面都有非常好的優(yōu)化效果。最后針對填充函數(shù)粒子群算法的缺陷進行加強改進,來增強粒子群算法的全局搜索實力,也使得計算速度明顯加快,以IEEE30節(jié)點系統(tǒng)為例用此算法進行無功優(yōu)化仿真驗算,同時為了驗證其應用的廣泛性,在含風電場的電力系統(tǒng)進行了仿真驗算,仿真結(jié)果充分證實了本課題提出的混合算法具備可靠性和實用效果。
[Abstract]:The optimization and adjustment of power system can promote the economic and smooth development of power system, and the use of reactive power optimization can effectively ensure the security of power system. It plays a very important role in the process of improving the quality of power supply and increasing economic benefits. Through the reactive power optimization of the power system, it should be possible to reduce the active power loss, improve the voltage quality and increase the stability of the power system. Particle swarm optimization (PSO) has made great achievements in solving power system optimization problems. However, PSO is easy to fall into local optimum, which limits its practical application. In this paper, a new power system optimization algorithm is proposed. That is, filled function particle swarm optimization (FPSO), and particle swarm optimization combined with filling function can effectively overcome the shortcomings of particle swarm optimization, achieve the purpose of global optimization, and solve the optimization problem more effectively. This paper first introduces the background of reactive power optimization research and summarizes the research status of the algorithms involved at home and abroad. Then, based on penalty function, the mathematical model of the objective function of reactive power optimization and the introduction of particle swarm optimization and filling function in the hybrid algorithm of reactive power optimization are presented, and then the simulation of reactive power optimization is carried out by MATLAB. The IEEE 14 bus system is used as an example to verify the proposed algorithm, and the results are compared with the results of the standard particle swarm optimization algorithm (PSO). The simulation results show that the hybrid PSO algorithm has very good performance in controlling node voltage and reducing system loss. Finally, aiming at the defects of the particle swarm optimization algorithm with filling function, the improvement is made to enhance the global search strength of the particle swarm optimization algorithm, and the calculation speed is obviously accelerated. The reactive power optimization simulation and checking calculation is carried out using this algorithm in the IEEE 30-bus system as an example. At the same time, in order to verify its wide application, the simulation and calculation are carried out in the power system with wind farm. The simulation results fully verify the reliability and practical effect of the hybrid algorithm proposed in this paper.
【學位授予單位】：上海電機學院
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM714.3

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