基于双适应度微粒群优化算法的最优潮流计算

在分析最优潮流(0PF)理论及其算法的基础上,引入微粒群优化算法(PSO).考虑到传统PSO算法应用罚函数处理OPF约束条件时容易造成对优良个体的湮灭,提出双适应度概念对微粒进行评估.利用双适应度PSO算法对算例进行分析并与其他算法比较,结果表明双适应度微粒群优化算法可较好处理最优潮流约束条件,在处理最优潮流问题上具有一定的有效性和优越性.

Optimal Power System Calculation Based on Particle Swarm Optimization of Double Fitness

The theory and algorithm of the optimal power flow (OPF) problem is analyzed. Traditional particle swarm optimization (PSO) algorithm is often used to deal with constraint condition using penalty function, but quality individuals are easily dropped out. A new PSO algorithm for the OPF problem is proposed. Double fitness that is used to evaluate the swarm is adapted to avoid the drawback of penalty function method. Experimental simulation is analyzed using the double fitness PSO method, and the method is compared with other optimization algorithms. The results show that the double fitness PSO method satisfies the constraint condition of OPF well. The investigations validate the effectiveness and advantage of this new method.