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将改进果蝇优化算法运用于无功优化领域,为电力系统的无功优化计算提供了一种新的算法.通过对迭代步长进行自适应调整可以有效避免果蝇算法可能陷入局部最优的问题,同时还能提高收敛精度.在无功优化模型中,对控制变量进行归一化处理,使得量纲一致;将约束条件以罚函数的形式并入目标函数中,实现对状态变量的限制.以IEEE30节点系统和IEEE57节点系统为例,分别基于果蝇优化算法(FOA)、改进果蝇优化算法(IFOA)和遗传算法(GA)进行了无功优化计算,结果表明改进果蝇优化算法(IFOA)具有更好的优化效果和计算速度,更加接近全局最优值,采用该算法解决无功优化问题效果很好.
The improved fruit fly optimization algorithm is applied in the field of reactive power optimization, which provides a new algorithm for power system reactive power optimization calculation. By adaptively adjusting the iteration step size, the fruit fly algorithm may be effectively prevented from falling into local optimum In the reactive power optimization model, the control variables are normalized so that the dimensions are consistent. The constraints are incorporated into the objective function in the form of penalty functions to achieve the limit of the state variables Taking the IEEE30 node system and the IEEE57 node system as an example, reactive power optimization based on the fruit fly optimization algorithm (FOA), modified fruit fly optimization algorithm (IFOA) and genetic algorithm (GA) are carried out respectively. The results show that the improved fruit fly optimization algorithm (IFOA) has better optimization results and computational speed, and is closer to the global optimal value. The algorithm is very effective in solving reactive power optimization problems.