新型的融合优化算法及其在电力系统中的应用

Novel hybrid optimization algorithm and its application in power system

提出了一种新型的融合优化算法,该算法结合了遗传算法(GA)的复制、交叉、变异操作以及粒子群优化算法(PSO)的个体速度和位置更新的原理,并将混沌的概念引入其中,它的性能要优于GA和PSO.在标准测试函数上进行了仿真比较,验证了新型算法的有效性.最后,这种新的融合优化算法被应用到了电力系统最优潮流的计算中,对IEEE-30系统进行仿真,并与遗传算法、标准PSO算法进行比较,结果表明新型的融合优化算法具有更好的优化性能.

This paper proposed a novel genetic algorithm （GA） particle swarm optimization （PSO） hybrid algorithm. The method combines the ideas of selection, crossover and mutation from GA and particle updating rules from PSO and chaotic dynamics. The effectiveness of the newly proposed algorithm is proven through simulations on benchmark tests, and, finally it is successfully applied into the optimal power flow problems. The proposed hybrid optimization method is demonstrated and compared with GA approach and standard PSO approach on the IEEE 30-bus system. The investigations reveal that the pro- posed method is more efficient in solving OPF problem.