电力系统无功优化的柯西粒子群算法

Cauchy Particle Swarm Optimization for Reactive Power Optimization

针对传统粒子群算法易陷入局部最优解、收敛速度慢的缺点,提出了柯西粒子群算法,并首次将其应用于电力系统无功优化问题。柯西粒子群算法是基于柯西分布的期望和方差均不存在的原理,对每一代粒子的全局极值进行柯西变异,以此来增加种群的多样性,扩大全局最优粒子的搜索区域,以尽快获得适应度更优的个体,从而可以避免算法陷入局部最优解,同时也加快了收敛速度。用柯西粒子群算法对IEEE-14节点标准测试系统进行无功优化计算,试验结果表明,与其他算法相比,柯西粒子群算法具有较强的全局寻优能力,且收敛速度快、鲁棒性好,能够更有效地解决电力系统无功优化问题。

Taking into the drawbacks of traditional particle swarm optimization （PSO） algorithm such as premature and slow searchspeed, Cauchy particle swarm optimization （CPSO） algorithm was proposed and first applied in reactive power optimization. CPSO is based on the principles of that the expectation and variance of Cauchy distribution is not exist, Cauchy mutation was applied to the global best particle of each generation in CPSO, it would increase the diversity of the population, extend the search space of the global partic）e, obtain the individual which has better fitness as quickly as possible, avoid the algorithm fall into local optima, also accelerate the convergence. The new algorithm was implemented on the IEEE-14 bus system and compared with other optimization algorithms＇ , the results show that HPSO has stronger global optimal searching ability, faster convergence rate, better robustness and can more effectively solve the reactive power optimization problem in power system.