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Application of Modified PSO in the Optimization of Reactive Power

机译:改进的粒子群优化算法在无功优化中的应用

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The basic PSO algorithm is a simple and rapid method, using evolution and calculation. However, the convergence precision of the basic PSO algorithm is low, and the algorithm can easily fall into its local maximum value. In order to overcome these disadvantages, an MPSO (modified particle swarm optimization) algorithm is proposed. The MPSO algorithm combines a mutation operator and a dynamically adjusted inertial factor which are respectively borrowed from different references. A group of optimal parameters are obtained based on experiments, then the algorithm adopts different parameters in different systems to meet the different demands. We test this MPSO algorithm on IEEE-6-bus system and IEEE-14-bus system. Compared with the basic PSO algorithm, the algorithm of MPSO performs better, and the convergence precision of the MPSO algorithm has been greatly improved.
机译:基本的PSO算法是一种简单快速的方法,它使用了进化和计算方法。然而,基本PSO算法的收敛精度较低,并且该算法很容易落入其局部最大值。为了克服这些缺点,提出了一种MPSO(改进粒子群算法)算法。 MPSO算法将变异算子和动态调整的惯性因子相结合,分别从不同的参考中借用。通过实验获得了一组最优参数,然后该算法在不同系统中采用不同的参数来满足不同的需求。我们在IEEE-6总线系统和IEEE-14总线系统上测试了该MPSO算法。与基本的PSO算法相比,MPSO算法具有更好的性能,大大提高了MPSO算法的收敛精度。

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