基于量子粒子群算法的电力系统无功优化

量子粒子群算法是以粒子群中粒子的收敛特性为基础,依据量子物理理论提出的,改变了传统粒子群算法的搜索策略,可使粒子在整个可行解空间中搜索寻求全局最优解.首次将量子粒子群算法用于电力系统无功优化中,以网损最小为目标函数,在IEEE30节点系统上进行测试,通过仿真测试以及不同算法优化结果的对比,表明基于量子粒子群(QPSO)...

Reactive Power Optimization in Power System Based on Quantum-behaved Particle Swarm Optimization

Quantum-behaved particle swarm optimization algorithm is based on the astringency of particles in particle swarm,which is presented according to quantum physics theory.It changes evolutionary search strategy of traditional particle swarm optimization algorithm.Particles can search global optimal solution in whole feasible solution.Quantum-behaved particle swarm optimization algorithm is firstly used in reactive power optimization in power system in this paper and applied for optimal reactive power is evaluated on an IEEE 30-bus power system.The modeling of reactive power optimization is established by taking the minimum network losses as the objective.The simulation results and the comparison results with various optimization algorithms demonstrate that the proposed approach converges to better solutions much faster than the current methods and the algorithm can make effectively use in reactive power optimization.Simultaneously,the validity and superiority of QPSO are proved.