基于遗传算法的电力系统无功优化

基于遗传算法的电力系统无功优化

目录

中文摘要 (1)

英文摘要 (2)

1 绪论 (3)

1.1 问题的提出及研究意义 (3)

1.2 国内外研究现状 (3)

1.3 本文的主要工作 (4)

2 电力系统无功优化模型 (6)

2.1无功优化的模型 (6)

2.2无功优化的目标函数 (6)

2.3无功优化的约束条件 (7)

3 遗传算法的原理及其解题过程 (9)

3.1 生物进化与遗传算法 (9)

3.2 遗传算法的特点及其优化原理 (9)

3.3 遗传算法的解题过程 (11)

4 算例分析 (14)

4.1 参数设置 (14)

4.2 结果分析 (16)

5 总结展望 (19)

参考文献 (20)

附录 (21)

摘要：随着现代工业的发展，电能质量越来越重要。无功优化是通过对可调变压器分接头、发电机端电压和无功补偿设备的综合调节，使系统满足电

网安全约束，在稳定电压的同时可以降低系统的网络损耗。由于可投切

并联电容器组的无功出力和可调变压器的分接头位置是非连续变化的，

因此电力系统无功优化问题是一个复杂的非线性混合整数规划问题、其

控制变量既有连续变量又有离散变量，优化过程十分复杂。针对无功优

化问题，人们提出了众多的求解方法，目前常用的、比较成熟的方法主

要有非线性规划法、线性规划法、混合整数规划法、人工智能法等。线

性规划法、非线性规划法均为单路径搜索方法，有可能会得到局部最优

解。为克服这一弊端，可以采用遗传算法，它从多个初始点出发进行搜

索，同一次迭代中各个点的信息互相交换，遗传算法允许所求解的问题

是非线性不连续的，并能从整个可行域空间寻找最优解。同时由于其搜

索最优解的过程是具有指导性进行的，从而避免了维数灾难问题。基于

以上优点本文采用了遗传算法对电力系统进行无功优化，在matlab上编

写程序对算例进行优化，优化结果表明算法的可行性。

关键字：电力系统；无功优化；非线性规划；遗传算法

Abstract: With the development of modern industry, power quality is becoming more and more important. Reactive power optimization is based on the

adjustable transformer tap, generator terminal voltage and reactive power

compensation equipment comprehensive regulation which can meet the

grid security constraints, and can reduce the system network loss while

stabilizing the voltage. Because of the reactive power output of the shunt

capacitor bank and the position of the tap of the adjustable transformer is

discontinuous the reactive power optimization problem of power system is

a complex nonlinear mixed integer programming problem. Its control

variables include continuous and discrete, and the optimization process is

very complicated. For the problem of reactive power optimization, many

methods have been put forward. The commonly used methods are

nonlinear programming method, linear programming method, mixed

integer programming method, artificial intelligence method, etc. The

linear programming method and the nonlinear programming method are

all single path search methods, and it will obtain the local optima. In order

to overcome the disadvantages of them we can use the genetic algorithm.

It starts from many initial points to search. The information can exchange

with each other in iteration. The genetic algorithm allows the solution of

the problem to be nonlinear and discontinuous, and can find the optimal

solution from the whole feasible domain space. At the same time, because

the process of searching the optimal solution is instructive, the curse of

dimensionality is avoided. Based on the above advantages, this paper

adopts the genetic algorithm to optimize the reactive power of the power

system. The program is written on the MATLAB to optimize the example,

and the optimization results show the feasibility of the algorithm.

Keyword:power system, reactive power optimization, nonlinear programming, genetic algorithm