Reactive power optimization based on Particle Swarm Optimization and Simulated Annealing cooperative algorithm

To avoid the increasing of the power loss and the declining quality caused by the imbalance of reactive power in power system, a method based on the co-evolution of Simulated Annealing algorithm (SA) and Particle Swarm Optimization (PSO) for reactive power optimization is established. PSO is a evolutionary computational method that optimizes a problem fast and effectively and it is suitable for handling real-value problem. But PSO algorithm also has some limitations such as premature convergence, which causes the bad accuracy of convergence. Some expansions and corrections including inertia weight and Simulated Annealing algorithm (SA) are introduced in this paper to improve the basic particle swarm optimization algorithm. Simulated annealing algorithm with the weight coefficient combined with particle swarm optimization (SA-WPSO) is proposed to solve reactive power optimization problem. Compared with the algorithms such as PSO and SA-PSO, SA-WPSO is better for global convergence and higher accuracy of reactive power optimization by using IEEE-10 bus system as a model for the simulation.