基于混合智能算法的无功优化及其应用研究

发布时间：2018-06-25 12:44

本文选题：电力系统 + 无功优化　；参考：《上海电机学院》2017年硕士论文

【摘要】：电力系统的优化和调整是可以促进电力系统经济和顺利的发展,利用无功优化能够有效地保证电力系统的安全性,在提升供电质量和增加经济收益的过程中发挥着非常重要的作用。通过电力系统的无功优化应当能够取得使有功损耗得到降低、电压质量得到提升以及使电力系统稳定性得到增加的效果。粒子群算法在求解电力系统优化问题上取得了很大的成果,但粒子群算法易陷入局部最优的缺点限定了其现实的应用,本课题研究提出一种新的电力系统的优化算法,即填充函数粒子群算法(FPSO),而结合填充函数的粒子群算法能有效克服粒子群算法的缺点,达到全局优化的目的,更有成效地求解优化问题。本课题首先介绍了无功优化研究的背景以及对其所涉及算法的国内外研究的现状进行了总结,然后以罚函数为基础提出了无功优化目标函数的数学模型及对其优化的混合算法中粒子群算法和填充函数法的介绍,接着采用MATLAB工具对无功优化进行研究仿真运算,以IEEE14节点系统为例进行有力的验证,同时和标准的粒子群算法的优化结果进行对比分析。得到的仿真结果可以证明,填充函数粒子群算法的混合方法在控制节点电压和减少系统网损的两个方面都有非常好的优化效果。最后针对填充函数粒子群算法的缺陷进行加强改进,来增强粒子群算法的全局搜索实力,也使得计算速度明显加快,以IEEE30节点系统为例用此算法进行无功优化仿真验算,同时为了验证其应用的广泛性,在含风电场的电力系统进行了仿真验算,仿真结果充分证实了本课题提出的混合算法具备可靠性和实用效果。
[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.
【学位授予单位】：上海电机学院
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM714.3

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