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针对微粒群算法在解决电力系统经济负荷分配(ED)时容易早熟收敛和后期收敛速度慢的特点,将梯度微粒群算法(GPSO)应用于ED问题的求解,算法考虑了机组的运行约束。本算法对在迭代过程中微粒的位置及适应度计算,获取梯度信息,进一步调整惯性权重,从而提高基本微粒群算法的收敛速度,避免其容易陷入局部极值。运用GPSO算法对IEEE30节点系统进行计算,并将其计算结果与微粒群算法(PSO)、梯度法和遗传算法(GA)进行比较,优化结果好于上述3种方法,分析表明该方法是可行的、有效的。
Aiming at the characteristics of particle swarm optimization (PSO), which is easy to premature convergence and slow convergence in the late stage of economic load distribution (ED), a gradient particle swarm optimization (GPSO) algorithm is applied to solving ED problems. The algorithm considers the operation constraints of the generating set. This algorithm calculates the position and fitness of the particles during iteration, obtains the gradient information and further adjusts the inertia weight, so as to improve the convergence rate of the basic particle swarm optimization and avoid its easy to fall into the local extreme. By using GPSO algorithm to calculate the IEEE30 node system and comparing the results with PSO, gradient method and genetic algorithm (GA), the optimization results are better than the above three methods, and the analysis shows that the method is feasible ,Effective.