| 粒子群优化算法及其在机组优化组合中应用 |
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| 引用本文： | 张振宇,葛少云,刘自发.粒子群优化算法及其在机组优化组合中应用[J].电力自动化设备,2006,26(5):28-31. |
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| 作者姓名： | 张振宇 葛少云 刘自发 |
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| 作者单位： | 天津大学,电气与自动化工程学院,天津,300072;天津大学,电气与自动化工程学院,天津,300072;天津大学,电气与自动化工程学院,天津,300072 |
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| 摘 要： | 应用粒子群优化(PSO)算法对电力系统的机组优化组合问题进行研究,介绍了算法原理,分析了算法中各个参数的不同取值对算法搜索能力和收敛速度的影响,并以常用的测试函数进行验证,建立了相应的数学模型,并以IEEE3机6节点电力系统为实例进行研究。分析结果表明,PSO算法较之常用的遗传算法和混沌优化等算法,在算法结构、计算时间、搜索区间控制以及收敛速度等方面具有较好的特性,验证了该方法的有效性。
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| 关 键 词： | 粒子群优化 智能优化算法 机组组合优化 |
| 文章编号： | 1006-6047（2006）05-0028-04 |
| 收稿时间： | 2005-08-01 |
| 修稿时间： | 2005-12-22 |
Particle swarm optimization algorithm and its application in unit commitment |
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| ZHANG Zhen-yu,GE Shao-yun,LIU Zi-fa.Particle swarm optimization algorithm and its application in unit commitment[J].Electric Power Automation Equipment,2006,26(5):28-31. |
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| Authors: | ZHANG Zhen-yu GE Shao-yun LIU Zi-fa |
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| Affiliation: | School of Electrical and Automation Engineering,Tianjin University,Tianjin 300072,China |
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| Abstract: | PSO(Particle Swarm Optimization) algorithm is applied to optimize the unit commit-ment of power system. With the principle introduced,the influence of PSO parameter setting on its searching capability and convergence speed is analyzed and then validated by usual test functions. Corresponding mathematic model is built up and used in a three - machine six - bus IEEE power system simulation. Compared with genetic algorithm and chaotic optimization,PSO is better in algorithmic structure,computing time,search area control,convergence speed and so on. The application is effective. |
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| Keywords: | particle swarm optimization intelligent optimization algorithm unit commitment optimization |
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