一种求解最优潮流问题的改进粒子群优化算法

An Improved Particle Swarm Optimization Algorithm for Optimal Power Flow Problem

提出了一种新的基于可行保留策略和变异算子的改进粒子群优化算法来求解最优潮流问题。可行保留策略将最优潮流问题的目标函数和约束条件分开处理,使得只有可行的解才能指导粒子飞行,避免了粒子在不可行域中的无效搜索,提高了算法的搜索效率;变异算子以预定的概率选择变异个体,对粒子的位置进行高斯变异操作,使得粒子可以有效避免陷入局部最优,增强了算法的全局搜索能力。通过 IEEE 30节点系统对该算法进行了测试,结果表明,对于复杂的最优潮流问题,该算法优于进化规划算法和常规的粒子群优化算法。

A novel improved particle swarm optimization （PSO） algorithm based on feasible retention strategy and mutation operator is proposed to solve optimal power flow （OPF） problems. The objective function and constraint conditions of OPF are separately processed so that only feasible solutions can guide particle flying and the invalid search of particles in infeasible region can be avoided, thus the searching efficiency of the algorithm is improved. The mutation individuals are selected by mutation operator according to predetermined probability; the particle positions are modified by Gaussian mutation, so the particles can avoid getting trapped into local optima and the global searching ability of the algorithm is enhanced. The proposed algorithm is tested by IEEE 30-bus system, and the test results show that it is superior to evolutionary programming and conventional PSO algorithm for complex OPF problem.