基于改进粒子群算法的电力系统无功优化

An Improved 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,提出一种基于信息分享策略的改进型粒子群算法,并首次将其应用于电力系统无功优化问题。改进的粒子群算法通过调整学习因子而获得合理有效的收敛速度;采用信息分享策略以保证种群的多样性;在位置的更新过程中加入扰动项,从而避免算法陷入局部最优解。用改进型粒子群算法对IEEE-14节点标准测试系统进行无功优化计算,实验结果表明:与其他算法相比,该改进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,能有效地解决电力系统无功优化问题。

Reactive power optimization in power system is a complex nonlinear combinatorial programming problem with multi-objective, multi-variable and multi- constraint. Taking into the drawbacks of traditional particle swarm optimization （PSO） algorithm such as pre- mature and slow search speed, based on information sharing strategy, an improved Particle Swarm Optimization （ISIPSO） algorithm was proposed and first applied in reactive power optimization in this paper. ISIPSO adjusts learning factors to obtain rational and effec- tive search speeds. Applying the information sharing strategy increases the diversity of the population. Introducing disturbance term to the process of location updating avoid the algorithm fall into local optima, also accelerate the convergence. The new algorithm was im- plemented on the IEEE-14 bus system and compared with other optimization algorithms, the results show that ISIPSO has stronger global optimal searching ability, faster convergence rate, and better robustness and can more effectively solve the reactive power optimization problem in power system.