基于启发式算法的电网无功优化研究

论文总字数：22590字

摘 要

Abstract Ⅱ

第一章 绪论 1

1.1引言 1

1.2 无功功率对电压的影响 1

1.3电力系统中的无功功率的平衡 3

1.4 无功功率优化的目的及意义 3

1.5 无功优化的研究现状 4

1.6 启发式算法的选择 5

第二章 无功优化数学模型 6

2.1 目标函数 6

2.2 相等约束 6

2.3 不等式约束 6

第三章 潮流计算 7

3.1 潮流计算 7

3.2 几种潮流算法 8

3.3.1牛顿-拉夫逊法 8

3.3.2 高斯-塞德尔法 9

3.3.3 P-Q分解法 10

3.3 算法比较及选择 10

3.4 本章小结 12

第四章 遗传算法在无功优化应用中的改进 13

4.1 遗传算法的基本原理 13

4.1.1 编码 13

4.1.2适应度函数 13

4.1.3 遗传算子 13

4.1.4 遗传算法参数设置 14

4.2 遗传算法的特点 15

4.3 改进遗传算法在无功优化中的应用 15

4.4 MATLAB语言 17

4.4.1 参考程序 18

第五章 结果与分析 21

5.1算例分析 21

5.1.1 IEEE-30节点系统设计 21

5.1.2 算例分析 22

第六章 结论 25

6.1 结论 25

6.2 展望 25

附录 IEEE-30节点系统参数（标么值） 26

致 谢 28

参考文献 29

基于启发式算法的电网无功优化研究

摘 要

电力系统的无功优化技术是确保系统安全和经济运转的有效方法和手段,也是改善电力系统的电压质量的主要措施。所谓的无功优化,就是在系统的各种结构参数和负荷状态都有预先规定的目标时,通过对某些被控制变量进行优化,所能够寻求的在完全满足各种指定的约束条件和技术需求的前提下,使得系统的特点某一个或者几个性能指标都达到了最优时的一种无功优化手段。

通过无功优化能取得显著的经济利益，使电能品质、系统运作的可靠性和经济效益出色的结合在一起，因而提升无功功率的前景十分有望。传统的优化算法依赖于精准的逻辑科学数理模型。算法所求最终解和选取的初始值密切相关。另外地,研究的无功优化问题拥有非连续变化数值时,解得的数值会有明显的区别。经过多年探索，各界学者已逐渐应用AI技术来寻找优化无功电能的方法。本课题旨在探讨求解改进启发式算法在无功优化中的应用。

简单启发式算法存在较大误差的缺点。它还有一个问题是当涉及无功能源优化问题时，计算比较费时，除了算法本来就有问题之外，另一个有影响的要素是需要繁复地进行计算。多次进行计算所花的时间与整个算法的计算时间消耗成正比。在研究了一般的潮流运算方法之后，本文采用了P-Q分解法。

关键词：无功优化；潮流计算；改进遗传算法

Research on Reactive Power Optimization of Power Grid Based on Heuristic Algorithm

Abstract

The reactive power optimization technology of the power system is an effective method and means to ensure the safe and economic operation of the system, and it is also the main measure to improve the voltage quality of the power system. The so-called reactive power optimization means that when the various structural parameters and load conditions of the system have predetermined goals, by optimizing certain controlled variables, what can be sought is to fully meet various specified constraints and technologies. Under the premise of demand, it is a reactive power optimization method when one or several performance indicators of the system have reached the optimum.

Significant economic benefits can be obtained through reactive power optimization, and the combination of power quality, system operation reliability and economic benefits is excellent, so the prospect of increasing reactive power is very promising. Traditional optimization algorithms rely on accurate logical scientific mathematical models. The final solution obtained by the algorithm is closely related to the initial value selected. In addition, when the reactive power optimization problem being processed has a non-continuously changing value, the result will be quite wrong. In recent years, researchers have gradually applied AI technology to find ways to optimize reactive energy. This topic aims to explore the application of solving improved heuristic algorithm in reactive power optimization.

The simple heuristic algorithm has the shortcoming of large error. Another problem is that when it comes to the optimization of passive sources, the calculation is relatively time-consuming. In addition to the inherent problems of the algorithm, another influential factor is the need for complicated calculations. The time spent for multiple calculations is proportional to the calculation time consumption of the entire algorithm. After studying the general power flow calculation method, this paper adopts the P-Q decomposition method.

Key words: Reactive Power Optimization; Flow Calculation; Improved Genetic Algorithm

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