【作者】 寸巧萍；

【导师】 李群湛；

【作者基本信息】 西南交通大学 ， 电力系统及其自动化， 2008， 硕士

【摘要】 随着国民经济的快速发展,各个行业对电能质量的要求不断提高。电力系统无功优化是保证系统安全、经济运行的一项有效手段,是降低网损、提高电压质量的重要措施。因此,电力系统无功优化问题的研究,既具有理论意义,又具有实际应用价值。电力系统的无功优化问题是一个多目标、多变量、多约束的混合非线性规划问题,其操作变量既有连续变量又有离散变量,使得优化过程十分复杂。本文介绍了电力系统无功优化领域的研究现状及其发展,概述了求解无功优化问题的方法,这些方法可以大致分成两类:经典方法和现代方法。经典方法主要指确定性搜索方法,现代方法包括人工智能方法(尤其是遗传算法)、禁忌搜索算法和模拟退火算法等。但这些方法都不同程度的存在一些缺点及它的局限性,因此本文深入地研究了无功优化的特点,建立了以有功网损为目标函数的电力系统无功优化数学模型,提出了一种应用于电力系统无功优化的新算法—量子遗传算法。量子遗传算法是将量子计算与遗传算法相结合的一种崭新的优化方法,用量子比特为基本信息位编码染色体,用基于量子概率门的量子变异实现个体进化,具有种群规模小、收敛速度较快,全局寻优能力强的特点,有很大的生命力和研究价值,能大大提高遗传算法的效率,弥补遗传算法的不足。将量子遗传算法应用于电力系统的无功优化问题中,通过MATLAB编制程序对IEEE-14和IEEE-30标准系统的测试表明,本文所提出的基于量子遗传算法的电力系统无功优化方法是正确有效的,比传统遗传算法收敛速度快、全局寻优能力强。同时,考虑到无功优化运行问题中电压稳定性的影响,在传统无功优化模型中引入静态电压稳定指标,建立了以网损最小、静态电压稳定裕度最大为目标的多目标无功优化模型,并将该算法用于IEEE-14系统,结果验证了模型和算法的有效性。更多还原

【Abstract】 With the development of national economy, the demands of power supply quality from all kinds of industries are increased. Reactive power optimization is one of the most important control methods to ensure power system operation securely and economically, and an effective measure to improve the voltage profile and reduce the transmission loss. Study the problem of reactive power optimization has the great significance in theory and practical application.Reactive power optimization is a large-scale nonlinear optimization problem with a large number of objects, variables and uncertain parameters, the operating variables include continuous and discrete variables, so the optimization becomes very complex. This paper introduces the process of development and research actuality of reactive power optimization in power system, summarizes the methods of reactive power optimization; The methods mainly include two kinds: sutra and modern. The sutra method mainly refers to definitive searchable method, the modern method involve the manpower intelligence method (especially the genetic algorithm), the tabu searchable method and the simulated anneal etc.But the methods have some defects and limitations in certain degree, this paper discusses the characteristics of reactive power optimization thorough, establishes the basic mathematical model and the object function of model is based on active power loss, and puts forward a novel algorithm-quantum genetic algorithm (QGA) which is applied to the problem of reactive power optimization of power system. Quantum Genetic Algorithm is a new optimum method that combines quantum computation with Genetic Algorithms. It uses quantum bit as coding which is different from conventional binary coding completely. QGA makes full use of superposition of quantum states to keep diversity of population and avoid prematurity of population. So it has good characteristics of strong search capability, rapid convergence and no premature. It appears strong life-force and valuable for research. It can greatly improve the computation efficiency of GA and remedy shortcoming.QGA is applied to the problem of reactive power optimization of power system, the computing results against the IEEE-14 and IEEE-30 criterion testing system prove that method of reactive power optimization based on QGA proposed in this paper is effective. Compared with the traditional genetic algorithm, QGA has good characteristics of strong search capability, rapid convergence and no premature, is superior to conventional genetic algorithms in quality and efficiency.This paper incorporates the voltage stability view to reactive power dispatch and control problem, a model of multi-objective reactive power optimization is established, which takes into account of loss minimization, voltage stability margin maximization and high service quality. The simulations are carried out on IEEE-14 bus system, and the results show the validity of the proposed model and algorithm.更多还原