改进PSO算法用于电力系统无功优化的研究  

Reactive Power Optimization of Power System Using the Improved Particle Swarm Optimization Algorithm

作　　者：袁松贵[1] 吴敏[1] 彭赋[1] 朱豆[2] 杨珏[2] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083 [2]长沙理工大学电气与信息工程学院,长沙410076

出　　处：《高电压技术》2007年第7期159-162,共4页High Voltage Engineering

基　　金：国家自然科学基金(60574014)~~

摘　　要：由于电力系统无功优化为一有多变量、多约束、非线性的组合优化问题,针对传统粒子群算法收敛精度不高、易陷入局部最优的缺点,提出了一种改进的算法:分别赋予传统算法中的粒子以不同的初始惯性权重,权重较大的粒子拓展搜索空间,惯性权重较小的粒子完成局部强化寻优的工作。用改进的PSO算法无功优化计算IEEE-14节点系统的结果表明:新算法不仅避免了惯性因子权重调整的困难,而且较好地协调了算法的局部与全局搜索能力,可较好地解决电力系统的无功优化问题。Reactive power optimization in power system is a typical non-linear optimization problem with characteristics of multi-objective, multi-constrained, non-linear combination and discreteness. Conventional mathematical programming techniques are inadequate and insufficient to the optimal operation of power systems due to the inherent complexity. A solution to reactive power optimization of power system via an improved particle swarm optimization algorithm is presented, particle swarm optimization is applied to reactive power optimization of power system, but the traditional particle swarm optimization is low in precision and easy in premature convergence, an improved algorithm is presented, variant initial inertia weight is assigned to initial particles, the high-weight particles are applied to exploit search band, the low-weights particles are applied to local intensification search. The improved algorithm has been tested in the standard IEEE-14 bus system, the results show that the improved PSO not only overcomes the difficulty of inertia weight adjustment, reduces the computational requirements, but also perfectly harmonizes the global and local exploration capabilities of PSO, preventing the search forming in local optima or converging hard, and can effectively solved reactive power optimization problem of power system.

关 键 词：电力系统 粒子群算法 无功优化 惯性权重 优化计算 网损 

分 类 号：TM714[电气工程—电力系统及自动化]