电力系统无功优化的多智能体粒子群算法研究

为了更好地实现无功功率最优控制和提高电压质量,在现有基础上,提出了引用多智能体粒子群优化算法（MAPSO）.该算法结合了JADE系统和粒子群优化技术,粒子间构建了三维球形环境.PSO种群中,每一个Agent相当于算法中的一个粒子,他们通过Agent间进行竞争与合作操作和自学习操作,吸收了PSO算法的进化机理,能够更快地,更精确地收敛到全局最优解.经IEEE 14节点系统校验,并且与基于Matlab的PSO算法进行比较,结果表明,该算法具有收敛速度快,计算精度高的优点。

Research on multi-agent particle swarm optimization algorithm for power system reactive power optimization

In order to realize optimal control of reactive power and enhancement of voltage quality, this paper pro- poses, on the basis of the existing algorithms. It is an algorithm combines JADE system and particle swarm optimization technology, and formulates the 3d spherical environment between particles. In PSO species, every Agent equals to a particle in algorithm. Through the competition between Agents and cooperative and auto-tuning operation, the evolution mechanism of PSO algorithm is absorbed to rapidly and accurately converge so as to get the global optimal solution. The algorithm is verified in IEEE 14 node system and is compared with PSO algorithm based on Maflab. The result shows that the algorithm enjoys advantages including rapid convergence and high calculation precision.