模拟渔夫捕鱼寻优算法在多目标无功优化中的应用

Application of optimization algorithm on simulating the fisher fishing in multi-objective optimal reactive power

模拟渔夫捕鱼寻优算法(Optimization algorithm on simulating the fisher fishing,SFOA)是模拟渔夫捕鱼行为习惯而提出的一种新的智能算法。通过对该算法的研究,并将SFOA算法用于求解电力系统多目标无功优化问题。无功优化模型采用计及网损和电压平均偏离两个指标,并提出了基于考虑用户偏爱区域把目标函数转化为约束条件的方法,它可以有效地处理多目标优化问题。由于无功优化是多变量优化问题,而SFOA算法在处理多维问题时寻优速度受到限制,因此,采用在方体内随机初始化方法,简化了移动搜索和收缩搜索在方体内的复杂搜索,提高了算法的搜索速度。通过IEEE-30节点和IEEE-57节点算例仿真结果表明,该算法有较好的全局搜索性能和较稳定的收敛速度,能有效提高系统运行的经济性和安全性。

Optimization algorithm on simulating the fisher fishing （SFOA） is a novel optimization algorithm, which is presented based on simulating the behavior and habit of fisher＇s fishing. By studying the algorithm, it is used for solving the power system multi-objective reactive power optimization （ORP） problem. The model of ORP includes line loss and average voltage offset as optimal target, and the method that can transform target function to constraint based on user reference region is proposed. ORP is an optimization problem with multi-variable, and optimization speed of SFOA would be restricted. So the method of random initialization in the cube is adopted, which simplifies the moving search and shrinking search in the cube and improve searching speed. The simulation results for IEEE-30 nodes and IEEE-57 nodes system show that SFOA has global search performance and steady convergence rates, which can improve the economics and safety of power system effectively.