【摘要】 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.更多还原

【Abstract】 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.更多还原