基于自适应模拟退火遗传算法的多目标最优潮流

Multi-objective optimal power flow based on adaptive simulated annealing genetic algorithm

采用自适应遗传算法来确定基本遗传算法的交叉率和变异率,保证遗传算法的收敛性。同时引入模拟退火法思想,通过拉伸目标函数的适应度使优秀个体在产生后代时具有明显的优势,从而加速寻优的过程,形成一种新的算法:自适应模拟退火遗传算法。应用该算法进行电力系统多目标最优潮流计算,IEEE30试验系统计算结果表明了该算法的灵活性和有效性。

This paper adopts adaptive genetic algorithm(AGA) to determine crossover ratio and mutation ratio of the simple genetic algorithm to make the algorithm converge efficiently. At the same time, simulated annealing algorithm(SAA)is introduced to modify the genetic algorithm fitness values in order to improve genetic algorithm selection operator, so can accelerate the algorithm search for optimal solution. A new algorithm called adaptive simulated annealing genetic algorithm(ASAGA) is presented. The proposed method canbe applied to solve multi-objective optimal power flow problems. The simulation results of an IEEE30 bus test system demonstrate that the method can model and deal with constraints easily and flexibly, reduce the computational requirements and prevent the search from being in local optimum or converging with difficulty near the global optimum.