基于遗传算法的施工用电无功补偿的研究

发布时间：2018-04-26 09:14

本文选题：施工用电 + 无功优化　；参考：《东北大学》2015年硕士论文

【摘要】：因城市建设的需要,建筑物越来越多,施工用电凸显的尤为重要。为了节约用电、降低电网损耗、提高电压合格率,无功优化也成为配电网使用过程中的一个必要阶段。因此,以提高电压水平和降低网络损耗为主要目标的施工用电配电网无功优化的研究被有关人员所关注。对于配电网的无功优化,目前已提出多种算法。本文重点研究了遗传算法在施工用电配电网无功优化中的应用。本文首先阐述了施工用电配电网无功优化研究的目的和意义,并介绍了几种无功优化的常用方法。本文根据施工用电配电网的实际情况,建立了无功优化的数学模型、目标函数,并考虑了系统网损、电压质量和无功补偿容量等因素,以潮流方程作为等式约束,以控制量和状态变量变化范围作为不等式约束。然后详细说明了常规遗传算法的基本原理、基本操作和应用,并在此基础上对常规遗传算法进行了改进。在常规遗传算法中,存在收敛速度慢、容易早熟收敛等问题,本文通过改进的遗传算法,解决这些问题。主要包括:采用十进制编码,提高了算法的计算能力;采用线性变化和指数变化规律的越界罚系数、对适应度函数进行线性变换,从而保持了种群的多样性;采用遗传因子自适应变化和改进的遗传操作,大幅度的提高了遗传算法的全局优化和局部寻优的能力。采用遗传算法与序列二次规划法的混合遗传算法,提高了整体算法的收敛性。仿真结果表明,改进的遗传算法在计算速度和收敛能力上明显优于常规遗传算法,在施工用电配电网无功优化运行中是可行并且有效的。
[Abstract]:Due to the needs of urban construction, more and more buildings, construction electricity is particularly important. In order to save electricity, reduce the loss of power grid and improve the qualified rate of voltage, reactive power optimization has become a necessary stage in the operation of distribution network. Therefore, the research on reactive power optimization of power distribution network with the aim of increasing voltage level and reducing network loss is concerned. A variety of algorithms have been proposed for reactive power optimization in distribution networks. This paper focuses on the application of genetic algorithm in reactive power optimization of construction power distribution network. In this paper, the purpose and significance of reactive power optimization in construction power distribution network are first expounded, and several common methods of reactive power optimization are introduced. According to the actual situation of power distribution network for construction, the mathematical model and objective function of reactive power optimization are established in this paper. The factors such as system network loss, voltage quality and reactive power compensation capacity are considered, and the power flow equation is taken as equality constraint. The range of control variables and state variables are taken as inequality constraints. Then, the basic principle, basic operation and application of conventional genetic algorithm are explained in detail, and the improvement of conventional genetic algorithm is made on this basis. In the conventional genetic algorithm, there are some problems such as slow convergence rate and easy premature convergence. This paper solves these problems through the improved genetic algorithm. It mainly includes: using decimal coding to improve the calculation ability of the algorithm, using linear and exponential change law of penalty coefficient, the fitness function of linear transformation, so as to maintain the diversity of the population; The ability of global optimization and local optimization of genetic algorithm is greatly improved by using genetic factor adaptive variation and improved genetic operation. The hybrid genetic algorithm (GA) and sequential quadratic programming (SQP) are used to improve the convergence of the whole algorithm. The simulation results show that the improved genetic algorithm is superior to the conventional genetic algorithm in computing speed and convergence ability, and it is feasible and effective in the optimal operation of reactive power in power distribution network.
【学位授予单位】：东北大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM714.3

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