| 引用本文: | 刘前进,许慧铭,施超,等.改进教与学方法在电力系统无功优化中的应用研究[J].电力系统保护与控制,2015,43(9):82-88. |
| LIU Qianjin,XU Huiming,SHI Chao,et al.Research on modified teaching-learning algorithm for reactive power optimization[J].Power System Protection and Control,2015,43(9):82-88 |
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| 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。 |
| 关键词: 教与学优化算法 无功优化规划 能量损失 多负荷水平 自适应小波变异 |
| DOI：10.7667/j.issn.1674-3415.2015.09.013 |
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| Research on modified teaching-learning algorithm for reactive power optimization |
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LIU Qianjin1, XU Huiming1, SHI Chao1, WEI Shengxuan2
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1.School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China;2.Guangxi Electric Power Industry Investigation Design and Research Institute, Nanning 530023, China
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| Abstract: |
| Taking the minimum total year energy loss as an objective function, a modified teaching-learning-based optimization (MTLBO) algorithm is proposed for reactive optimization of power systems. The proposed teaching-learning optimization algorithm is a new population-based optimization method which includes two phases: teacher phase and learner phase. To overcome local convergence, a new phase called “modified phase” based on a self-adaptive wavelet mutation strategy is added to the algorithm to improve the performance. The proposed method is applied to IEEE-30 bus test system and the simulation results are compared with other algorithms which verify the superiority of the proposed method. It shows that this method is an effective method for large-scale power system application. |
| Key words: teaching-learning-based optimization algorithm optimal reactive power planning energy loss multi-load levels self-adaptive wavelet mutation |
