混沌模拟退火算法在无功优化中的应用

为了更有效地改进处理无功优化问题的方法,提出了混沌模拟退火(CSA)算法,该算法是一种基于混沌变量的改进模拟退火算法,结合了混沌算法的全局遍历性和模拟退火算法的启发式规则,在模拟退火算法的搜索过程中加入了混沌算法的优点。利用混沌算法确定算法的初始温度,有效地减小了搜索空间,同时利用混沌算法确定模拟退火算法中的扰动准则,使算法有效跳出局部最优解。最后将混沌模拟退火算法应用于电力系统无功优化中,通过对IEEE 6和IEEE 30节点以及实际129节点系统的仿真验证了该算法应用的有效性。

Application of Chaos Simulation Annealing in Power System Reactive Optimization

Power system reactive optimization is a nonlinear,multi-variates,complex global optimal problem.This paper presents an improved algorithm for power system reactive optimization which is based on the Simulated Annealing and Chaos algorithm.Simulated Annealing is the general algorithm for nonlinear optimal problems and it can obtain the global optimal solution for object function based on heuristics and random search technology and also with a strong local search capability.A suitable initial temperature is important for improving convergence speed in the process of Simulated Annealing.Chaos Simulated Annealing is an improved Simulated Annealing based on Chaotic variables(CSA),which integrates the global ergodicity of Chaos Algorithm and the heuristic rules of Simulated Annealing.The merits of Chaos Algorithm are added in the process of Simulated Annealing to improve the global search capability.The hybrid algorithm can reduce the search space effectively with a reliable initial temperature defined by Chaos Algorithm.A suitable interference rule is defined using the simulated Chaos.And in the process of evolution,the local search capability can be improved by decreasing the interference amplitude.The Chaos-simulated annealing is applied in reactive optimization of power system,its effectiveness is proved by the simulation results of IEEE 6-bus,IEEE 30-bus and an actual 129-bus system and has a shorter calculating time to find global optimal solution.