基于分布式协同粒子群优化算法的电力系统无功优化

该文提出一种新颖的用于求解无功优化问题的分布式协同粒子群优化算法.考虑到大规模电力系统集中优化难度较大,采用分层控制中的分解-协调思想将大系统分解成若干个独立的子系统,有效地降低求解问题的复杂度,并采用混合策略在各子系统问进行协同进化.此外,子系统的无功优化采用了一种改进的粒子群优化算法,考虑了更多粒子的信息,能有效地提高算法的收敛精度和计算效率.对4个不同大小规模的系统进行的仿真计算结果表明该文提出的方法能够获得高质量的解,并且计算时间短,效率高,适合求解大规模电力系统的无功优化问题.

DISTRIBUTED COOPERATIVE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REACTIVE POWER OPTIMIZATION

This paper presents a novel distributed cooperative particle swarm optimization algorithm for optimal reactive power dispatch and voltage control of power system. Considering the large-scale characteristics in practical system, this paper employs decomposition-coordination theory in hierarchical control structure. Meanwhile, the reactive power optimization problem is decomposed into a number of sub-problems. These sub-problems interact with each other through a hybrid strategy, and reactive power optimization separately in these sub-problems uses an improved particle swarm optimization algorithm, which considers more particle information to control the mutation operation. The proposed method applied for optimal reactive power dispatch is evaluated on four various scale power systems Simulation results show that higher quality solutions are obtained in a shorter time by the proposed approach than by SGA and PSO, and the proposed approach with a hybrid strategy is very suitable for solving large-scale power system reactive optimization problems.