| 基于遗传模拟退火算法的最大功率点跟踪研究 |
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| 引用本文： | 王亚楠,康英伟,郑鹏远,彭道刚.基于遗传模拟退火算法的最大功率点跟踪研究[J].上海电力学院学报,2016,32(3):252-256,273. |
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| 作者姓名： | 王亚楠 康英伟 郑鹏远 彭道刚 |
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| 作者单位： | 上海电力学院;上海发电过程智能管控工程技术研究中心,上海电力学院;上海发电过程智能管控工程技术研究中心,上海电力学院;上海发电过程智能管控工程技术研究中心,上海电力学院;上海发电过程智能管控工程技术研究中心 |
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| 基金项目： | 上海市自然科学基金(15ZR1418600);上海市科学技术委员会工程技术研究中心资助项目(14DZ2251100). |
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| 摘 要： | 通过分析太阳能光伏发电系统的工作特征和现有的最大功率点跟踪(MPPT)方法,提出了一种基于遗传模拟退火算法的光伏发电系统MPPT方法.该算法将遗传算法和模拟退火算法相结合,通过将局部搜索过程引入遗传算法,从而使两种算法的搜索能力得到互相补充.针对某光伏发电系统的MPPT问题,通过仿真,将遗传模拟退火算法和遗传算法进行比较.仿真结果显示,遗传模拟退火算法和传统的遗传算法相比,能更快速、精确地跟踪到光伏系统的最大功率点.
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| 关 键 词： | 光伏发电 最大功率点跟踪 遗传模拟退火算法 |
| 收稿时间： | 2015/9/29 0:00:00 |
Research of Maximum Power Point Tracking Based on Genetic Simulated Annealing Algorithm |
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| WANG Yanan,KANG Yingwei,ZHENG Pengyuan and PENG Daogang.Research of Maximum Power Point Tracking Based on Genetic Simulated Annealing Algorithm[J].Journal of Shanghai University of Electric Power,2016,32(3):252-256,273. |
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| Authors: | WANG Yanan KANG Yingwei ZHENG Pengyuan and PENG Daogang |
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| Affiliation: | Shanghai University of Electric Power, Shanghai 200090, China;Shanghai Engineering Research Center of Intelligence Management and Control for Power Process, Shanghai 200090, China,Shanghai University of Electric Power, Shanghai 200090, China;Shanghai Engineering Research Center of Intelligence Management and Control for Power Process, Shanghai 200090, China,Shanghai University of Electric Power, Shanghai 200090, China;Shanghai Engineering Research Center of Intelligence Management and Control for Power Process, Shanghai 200090, China and Shanghai University of Electric Power, Shanghai 200090, China;Shanghai Engineering Research Center of Intelligence Management and Control for Power Process, Shanghai 200090, China |
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| Abstract: | By analyzing the working characteristics of the solar photovoltaic power generation system and the Power Point Tracking Maximum (MPPT) method,a new MPPT method based on genetic simulated annealing algorithm is proposed.The algorithm combines genetic algorithm and simulated annealing algorithm,and the local search process is introduced into genetic algorithm so that the search ability of the two algorithms is complementary to each other.For the MPPT problem of a photovoltaic power generation system,through simulation,the genetic simulated annealing algorithm results are compared with the simulation results of genetic algorithm.The simulation results show that compared with the traditional genetic algorithm,the genetic simulated annealing algorithm can track the maximum power point of the PV system quickly and accurately. |
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| Keywords: | photovoltaic power generation maximum power point tracking genetic simulated annealing algorithm |
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