| 引用本文: | 陈建华,李先允,邓东华,廖德利.粒子群优化算法在电力系统中的应用综述[J].电力系统保护与控制,2007,35(23):77-84.[点击复制] |
| CHEN Jian-hua,LI Xian-yun,DENG Dong-hua,LIAO De-li.A review on application of particle swarm optimization in electric power systems[J].Power System Protection and Control,2007,35(23):77-84[点击复制] |
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| 摘要: |
| 粒子群优化（PSO）算法是一种新兴的群体智能优化技术，其思想来源于人工生命和演化计算理论，PSO通过粒子追随自己找到的最优解和整个群的最优解来完成优化。该算法简单易实现，可调参数少，已得到广泛研究和应用。在大量参阅国内外相关文献的基础上，简要介绍了PSO算法的工作原理，较为全面地详述了粒子群优化方法在电力系统中的应用，如电网规划、检修计划、短期发电计划、机组组合、负荷频率控制、最优潮流、无功优化、谐波分析与电容器配置、参数辨识、状态估计、优化设计等方面，并对今后可能的应用指出了研究方向。 |
| 关键词: 粒子群优化 群体智能 状态估计 参数辨识 |
| DOI：10.7667/j.issn.1674-3415.2007.23.020 |
| 投稿时间：2007-05-09修订日期：2007-08-01 |
| 基金项目:南京工程学院科研基金项目（KXJ06－028） |
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| A review on application of particle swarm optimization in electric power systems |
| CHEN Jian-hua,LI Xian-yun,DENG Dong-hua,LIAO De-li |
| (Dept of Electric Power Engineering , Nanjing Institute of Technology , Nanjing 211167,China) |
| Abstract: |
| Particle swarm optimization (PSO) is a new swarm intelligence optimization technique originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm. PSO can be implemented with ease and few parameters need to be tuned. It has been successfully applied in many areas. Based on the inspection of a large number of domestic and foreign literature, the basic principles of PSO are presented, the main research results of applying PSO in following aspects relevant to electric power systems, such as power system expansion planning, maintenance scheduling, short-term generation scheduling, unit commitment , load frequency control, optimal power flow, reactive power optimization, harmonic analysis and capacitor configuration, parameter identification, state estimation and optimal design, are overall presented in detail in this paper. The research trends towards the application in the future is predicated. |
| Key words: particle swarm optimization swarm intelligence state estimation parameter identification |
