电力系统无功优化智能新方法及其应用

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3.0 侯斌 2024-11-19 6 4 1.53MB 58 页 15积分

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摘要

随着近年来全球资源环境压力的不断增大，电力市场化进程的不断发展，用

户对电能质量的可靠性和经济性要求也在不断提升。而在电力系统的迅速发展过

程中，日趋庞大与复杂的电网结构使得系统的稳定性问题尤为突出。为此，具有

灵活、安全、经济等性能的智能电网成为了未来电网的发展方向，而无功优化问

题是智能电网研究中的一个重要环节，因此，在智能电网的建设过程中，对当今

电力市场环境下无功优化的研究势在必行。

由于我国一直以来所采用的无功优化的技术水平比较低，一些变电站虽然安

装了自动调节装置，但由于控制策略的设计不尽合理，在实际运行中未能充分发

挥作用，开关投切电容器的投切速度低、调节性能差、没有应对高次谐波的能力、

电力设备寿命短，再者就是无功优化技术自动化程度低，不能满足电网无功优化

控制的调度要求，因此在智能电网的建设规划中，无疑将对电力系统无功优化问

题进行智能化处理。

电力系统无功的合理分布是提高电压质量和降低网损的前提条件，电力系统

中无功功率的优化调整，将对于整个电力系统的安全经济运行产生重要作用。通

过对电网进行无功优化可以改善系统的电压质量、降低系统的运行网损、提高系

统运行的安全稳定性，同时有效提高电网的输送能力和设备利用率，使整个电力

系统的技术经济性能指标得到有效改善。

近年来，群体智能优化算法在电力系统无功优化的理论研究与工程应用方面

已取得了显著成效。本文对群体智能优化算法中的基本粒子群算法的原理进行了

详细介绍，并给出了粒子群算法求解电力系统无功优化问题的流程图。同时针对

基本粒子群算法易陷入局部最优且收敛速度慢等缺点，加之电力系统无功优化问

题本身的复杂性，首次提出将一种柯西粒子群优化算法用于电力系统无功优化问

题，该算法将基本粒子群算法与柯西变异结合起来，以此来增加种群的多样性，

避免算法陷入局部最优，在本文中，首次将柯西粒子群算法应用于电力系统无功

优化问题。

本文针对电力系统无功优化问题非线性、多变量、多约束的特点，建立了以

网损最小为目标，符合实际约束条件和无功调节手段的无功优化数学模型。对优

化问题中的状态变量采用罚函数的形式进行处理，有效解决了发电机节点无功出

力和负荷节点电压越限情况。本文采用柯西粒子群算法解决电力系统无功优化问

题，并通过 matlab 编程实现，以 IEEE-14 节点系统为例，对于几种不同的智能优

化算法的仿真结果进行比较，实验表明，柯西粒子群算法在解决电力系统无功优

化问题方面具有良好的理论价值和工程应用价值。同时，本文采用 VB 进行编程、

界面设计，同时结合 SQL server，进行软件设计，实现了系统对于采集数据的实时

计算功能。该软件使电力系统无功优化操作更为简便、直观，具有很好的市场前

景。

关键词：智能电网电力系统无功优化粒子群算法罚函数柯西变异

ABSTRACT

In recent years, with the increasing pressure on global resources and environment,

and the development of the electric power market process, and the requirements of the

electric power’s quality reliability and economical efficiency are constantly rising, while

in the rapid development process of power system, increasingly large and complex

power grid structure makes the system stability is particularly outstanding. Therefore,

the intelligent power grid with flexible, security, economic and other properties become

the future development direction of power grid, and reactive power optimization is an

important link of intelligent power grid research, therefore, in the course of the

construction of the smart grid, research on reactive power optimization of current

electric power market environment is imperative.

Since China has been adopted the reactive power optimization technology with low

level, although some substation installed the automatic adjustment device, some device

failed to bring into full play in the actual operation with irrational control strategy. Low

switching speed of switch, poor adjust performance, non-ability of cope with high

harmonics, short life of electrical power equipment, and the lower automation level of

reactive power optimization can’t meet the scheduling requirements of reactive power

optimization control in power grid, so in construction planning of intelligent power grid,

reactive power optimization will be processed intelligently.

The reasonable distribution of reactive power is a prerequisite of improving voltage

quality and reducing losses, that will be play an important role in safe and economic

operation of entire power system. Through the reactive power optimization to power

grid can improve the system voltage quality, reduce the operating losses of system, and

improve the security and stability of system, at the same time, it can improve the grid

transmission capacity and equipment utilization of power grid, so that the technical and

economic performance of the whole power system has been effectively improved.

In recent years, the group intelligent optimization algorithm has made a significant

progress in the theory research and engineering application of reactive power

optimization of power system. This paper made a detailed description to basic particle

swarm theory that is one kind of group of intelligent optimization algorithms, and gives

the flow chart of the particle swarm optimization to solve reactive power optimization.

And for the disadvantages such as easy to fall into the local optimal and slow

convergence of particle swarm optimization, and the complexity of the problem itself of

reactive power optimization, this paper is the first to proposed using Cauchy particle

swarm optimization to solve reactive optimization problem of power system, this

algorithm combined basic particle swarm optimization with Cauchy mutation, in order

to increase the swarm’s diversity and avoid algorithm getting into local optimal, and the

Cauchy particle swarm optimization was applied to reactive power optimization of

power system for the first time in this paper.

In this paper, for the features of non-linear, multivariate, multi-constraint of reactive

power optimization problem, a reactive power optimization model with realistic

constraints and reactive power regulation means was founded. The phenomenon of the

reactive power of generator and the voltage-limit of load nodes was solved effectively

by processing the state variables in the form of penalty function. This paper adopted

Cauchy particle swarm optimization algorithm to solve reactive power optimization

problem, and programmed by matlab, took the IEEE-14 node system to make test, and

compared the simulation results of several different intelligent optimization algorithm.

The results showed that the Cauchy particle swarm optimization algorithm has a good

theoretical and engineering value for solving reactive power optimization problem. At

the same time, this paper made software design by using VB to program, making

interface design, and combining with SQL, then achieved the function of real-time

calculation to acquisition data of system. This software made de operation of reactive

power optimization easier, intuitive, and a good market prospects.

Key words: smart grid, reactive power optimization, particle swarm

optimization, penalty function, Cauchy mutation

第一章绪论 .......................................................... 1

§1.1 电力系统无功优化的基本概念及背景意义 ....................... 1

§1.1.1 基本概念 .............................................. 1

§1.1.2 背景及意义 ............................................ 1

§1.2 无功优化技术的发展概况 ..................................... 2

§1.2.1 传统优化算法 .......................................... 2

§1.2.2 智能优化算法 .......................................... 5

§1.3 本论文主要工作 ............................................. 8

§1.4 本章小结 ................................................... 8

第二章无功优化问题的数学模型 ....................................... 10

§2.1 无功优化数学模型 .......................................... 10

§2.1.1 目标函数 ............................................. 10

§2.1.2 等式约束 ............................................. 11

§2.1.3 不等式约束 ........................................... 11

§2.2 电力系统潮流分析 .......................................... 12

§2.3 牛顿—拉夫逊法介绍 ........................................ 12

§2.4 本章小结 .................................................. 15

第三章群体智能优化算法在电力系统无功优化中的应用 ................... 16

§3.1 基本粒子群优化算法介绍 ..................................... 16

§3.1.1 基本原理介绍 ......................................... 16

§3.1.2 参数分析 ............................................. 19

§3.1.3 实现步骤 ............................................. 20

§3.1.4 基本流程图 ........................................... 21

§3.1.5 程序设计 ............................................. 21

§3.2 柯西粒子群算法介绍 ......................................... 23

§3.2.1 柯西分布简介 ......................................... 23

§3.2.2 柯西粒子群算法基本原理 ............................... 23

§3.2.3 操作步骤 ............................................. 25

§3.2.4 流程图 ............................................... 27

§3.3 智能优化算法在电力系统无功优化问题中的应用 ................ 27

§3.3.1 优化问题基本概念 ..................................... 27

§3.3.2 算法变量的编码 ....................................... 27

§3.3.3 离散变量的处理 ....................................... 28

§3.3.4 惩罚因子 ............................................. 28

§3.3.5 基本 PSO 算法用于电力系统无功优化 ..................... 28

§3.3.6 CPSO 算法用于电力系统无功优化 ....................... 32

§3.4 本章小结 .................................................. 34

第四章算例分析 ..................................................... 35

§4.1 IEEE-14 节点系统基本情况 ................................... 35

§4.2 算法参数设置 .............................................. 37

§4.3 仿真结果与分析 ............................................ 37

§4.4 本章小结 .................................................. 40

第五章应用软件开发 ................................................. 41

§5.1 界面设计 ................................................... 41

§5.2 混合编程 ................................................... 41

§5.3 无功优化计算及数据库的链接 ................................ 42

§5.4 优化结果 .................................................. 44

§5.5 面向对象的图形编程 ........................................ 45

§5.6 基本工作原理示意 .......................................... 46

§5.7 本章小结 .................................................. 46

第六章总结及展望 ................................................... 47

参考文献 ............................................................ 49

致谢 ............................................................... 54

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摘要：

摘要随着近年来全球资源环境压力的不断增大，电力市场化进程的不断发展，用户对电能质量的可靠性和经济性要求也在不断提升。而在电力系统的迅速发展过程中，日趋庞大与复杂的电网结构使得系统的稳定性问题尤为突出。为此，具有灵活、安全、经济等性能的智能电网成为了未来电网的发展方向，而无功优化问题是智能电网研究中的一个重要环节，因此，在智能电网的建设过程中，对当今电力市场环境下无功优化的研究势在必行。由于我国一直以来所采用的无功优化的技术水平比较低，一些变电站虽然安装了自动调节装置，但由于控制策略的设计不尽合理，在实际运行中未能充分发挥作用，开关投切电容器的投切速度低、调节性能差、没有应对高次谐波的能力、电力...

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