电力系统无功优化多目标处理与算法改进

电力系统无功优化属于典型的多目标非线性复杂优化问题，求解非常困难。近年来，众多智能优化算法应用于该问题，其中粒子群优化（Particle Swarm Optimization，PSO）算法最具代表性；但PSO算法性能仍有待提高，如可能陷入局部极值。提出一种多策略融合粒子群优化（Particle Swarm Optimization with Multi-strategy Integratiofi，MSI—PSO）算法，对速度更新公式引入选择操作，分阶段加速因子调整和惯性权重动态调整，以平衡粒子局部搜索与全局探索能力；同时，随机选取部分性能差的粒子，将其速度更新公式中的个体认知部分修改为社会认知...

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Bibliographic Details
Published in电力系统保护与控制 Vol. 42; no. 5; pp. 129 - 135
Main Author 陈前宇 陈维荣 戴朝华
Format Journal Article
LanguageChinese
Published 西南交通大学电气工程学院,四川 成都,610031 2014
Subjects
人工智能
多目标无功优化
多策略融合粒子群优化算法
有功损耗
电压稳定
多目标无功优化
多策略融合粒子群优化算法
有功损耗
人工智能
multi-objective reactive power optimization
active network loss
particle swarm optimization with multi-strategy integration algorithm
voltage stability
电压稳定
artificial intelligence
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ISSN1674-3415

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Summary:电力系统无功优化属于典型的多目标非线性复杂优化问题，求解非常困难。近年来，众多智能优化算法应用于该问题，其中粒子群优化（Particle Swarm Optimization，PSO）算法最具代表性；但PSO算法性能仍有待提高，如可能陷入局部极值。提出一种多策略融合粒子群优化（Particle Swarm Optimization with Multi-strategy Integratiofi，MSI—PSO）算法，对速度更新公式引入选择操作，分阶段加速因子调整和惯性权重动态调整，以平衡粒子局部搜索与全局探索能力；同时，随机选取部分性能差的粒子，将其速度更新公式中的个体认知部分修改为社会认知部分，以提高算法搜索精度和收敛速度。建立以系统网络损耗最小和系统电压稳定裕度最大为目标的无功优化仿真模型，分别考虑加权法、隶属度函数法和Pareto法实施多目标处理。针对IEEE30节点测试系统进行仿真实验，结果表明，和其他几种改进PSO算法以及基于pareto最优解集PSO算法进行对比，所提MSI-PSO算法具有更好的性能，能够有效求解电力系统多目标无功优化问题。
Bibliography:multi-objective reactive power optimization; voltage stability; active network loss; artificial intelligence; particle swarmoptimization with multi-strategy integration algorithm.
CHEN Qian-yu, CHEN Wei-rong, DAI Chao-hua （School of Electrical Engineering, Southwest J iaotong University, Chengdu 610031, China）
41-1401/TM
Reactive power optimization is a typical multi-target nonlinear optimization problem, which is complex and difficult to solve. In recent years, many intelligent optimization algorithms are applied to solve the problem. The particle swarm optimization （PSO） algorithm is one of the most typical reactive power optimization intelligent optimization algorithms, while it still needs to be improved because it is easy to fall into local minima. This paper proposes an algorithm of particle swarm optimization with multi-strategy integration （MSI-PSO）. Selection operation, phased adjustment of acceleration factor and the dynamic adjustment of inertia weight are introduced to the speed updating formula to b
ISSN:1674-3415