基于学习策略的遗传算法在最优潮流中的应用

最优潮流问题是电力系统中一个重要的问题,从数学角度上讲,它是一个非线性规划问题。提出了一种基于学习策略的遗传算法用于解决最优潮流问题。学习策略使得种群中的普通个体可以向优良个体学习其优秀的基因结构,从而提高了个体的适应度,加快了算法的寻优速度,增强了算法的搜索能力。该算法中还采用排挤策略来避免个体的过度拥挤,增强了算法的全局搜索能力。通过算例验证了算法的可行性和有效性。

Application of Genetic Algorithm Based on Learning Strategy for Optimal Power Flow

Optimal power flow(OPF) is a very important problem in power system.Analyzing of OPF from mathematics aspect,it is a nonlinear programming problem.This paper suggests a genetic algorithm based on learning strategy to resolve the optimal power flow problem.The learning strategy makes the common individuals in the population learn the excellent gene structures from other fine individuals,whereby improving the fitness of individuals,accelerating the optimization speed and enhancing the local search ability of the algorithm.The crowding strategy is adopted in this algorithm to avoid the individual excess crowding and to enhance the global search ability.Also,the algorithm feasibility and validity are tested via the computation examples.