改进教与学方法在电力系统无功优化中的应用研究
发布时间:2018-11-12 17:12
【摘要】:以多负荷水平的全年能量损失最小为目标函数,提出一种改进教与学优化方法求解电力系统无功优化问题。教与学方法是一种新颖的无控制参数的群智能算法,包括教阶段和学阶段。为了克服局部收敛,改进教与学方法在此基础上提出一种基于自适应小波变异策略的改进阶段改善算法的性能并在IEEE-30节点系统进行仿真。结果与其他算法进行比较,验证了该算法的优越性。表明该方法是大规模电力系统可推广使用的有效方法。
[Abstract]:Taking the minimum annual energy loss at multi-load level as the objective function, an improved teaching and learning optimization method is proposed to solve the reactive power optimization problem in power system. Teaching and learning method is a novel swarm intelligence algorithm without control parameters, including teaching and learning stages. In order to overcome the local convergence, the improved teaching and learning method based on adaptive wavelet mutation strategy is proposed to improve the performance of the improved algorithm in the IEEE-30 node system. Results compared with other algorithms, the superiority of this algorithm is verified. It is shown that this method is an effective method for popularizing large scale power systems.
【作者单位】: 华南理工大学电力学院;广西电力工业勘察设计研究院;
【分类号】:TM714.3
[Abstract]:Taking the minimum annual energy loss at multi-load level as the objective function, an improved teaching and learning optimization method is proposed to solve the reactive power optimization problem in power system. Teaching and learning method is a novel swarm intelligence algorithm without control parameters, including teaching and learning stages. In order to overcome the local convergence, the improved teaching and learning method based on adaptive wavelet mutation strategy is proposed to improve the performance of the improved algorithm in the IEEE-30 node system. Results compared with other algorithms, the superiority of this algorithm is verified. It is shown that this method is an effective method for popularizing large scale power systems.
【作者单位】: 华南理工大学电力学院;广西电力工业勘察设计研究院;
【分类号】:TM714.3
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