| 引用本文: | 刘前进,许慧铭,施超.基于人工蜂群算法的多目标最优潮流问题的研究[J].电力系统保护与控制,2015,43(8):1-7.[点击复制] |
| LIU Qianjin,XU Huiming,SHI Chao.Research on power flow optimization based on multi-objective artificial bee colony algorithm[J].Power System Protection and Control,2015,43(8):1-7[点击复制] |
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| 摘要: |
| 以污染气体排放量、网损最小为目标，建立多目标电力系统最优潮流数学模型，并提出一种基于人工蜂群的多目标算法对其进行求解。该算法利用外部存档技术来保存进化过程中已经找到的Pareto最优解，并在每次迭代后更新。最后根据模糊集理论从Pareto最优解集中选取最优折衷解,为决策者提供科学的决策依据。通过IEEE-30节点系统及IEEE-57节点系统的仿真，验证了该算法在求解大规模电力系统多目标问题上的有效性，相比其他多目标算法能有效避免局部收敛。 |
| 关键词: 最优潮流 无功优化 人工蜂群算法 多目标 污染气体排放 |
| DOI：10.7667/j.issn.1674-3415.2015.08.001 |
| 投稿时间：2014-06-05修订日期：2015-01-08 |
| 基金项目: |
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| Research on power flow optimization based on multi-objective artificial bee colony algorithm |
| LIU Qianjin,XU Huiming,SHI Chao |
| (School of Electric Power, South China University of Technology, Guangzhou 510640, China) |
| Abstract: |
| Taking the minimum pollutant emission and active loss as objective functions, this paper builds a multi-objective optimization model for power flow optimization of power system. In the proposed algorithm, an external archive of non-dominated solutions is kept which is updated at each iteration. Moreover, a method based on fuzzy set theory is employed to extract one of the Pareto-optimal solutions set as the best compromise one to provide the scientific decision basis for decision-makers. Simulation of IEEE-30 bus system and IEEE-57 bus system testify that this algorithm can avoid the local convergence effectively compared with other multi-objective optimization algorithm. |
| Key words: optimal power flow reactive power optimization artificial bee colony multi-objective pollutant emission |
