基于模拟退火PSO的电力系统无功优化

对粒子群优化算法方法进行改进,把模拟退火机制引入到粒子群优化算法方法中,提出了基于模拟退火粒子群优化PSOSA(PSO with Simulated Annealing)算法,通过适当选择种群大小、调整惯性权重系数ω和退火系数C,以温度的缓慢下降来控制粒子的寻优过程,提高了粒子群优化算法的全局收敛性,改善了粒子的局部搜索能力.建立了以网损最小为目标的电力系统无功优化模型.通过对IEEE-30系统的无功优化计算,结果表明,PSOSA算法具有更好的全局收敛性和良好的搜索能力.

Reactive power optimization in power system based on PSO with simulated annealing

Particle swarm optimization (PSO) method is improved by introducing the mechanism of simulated annealing to original PSO, so as to propose a PSOSA (PSO with simulated annealing) method.It controls the optimal process of swarm by means of decreasing temperature slowly. Inertia coefficient and anneal coefficient are adjusted properly; so both the convergence and the local search ability are enhanced. The method is applied to the reactive power optimization and calculated for the IEEE 30-bus power system; the result shows that the approach can get better performance and solutions.