基于遗传模拟退火算法的最大功率点跟踪研究

王亚楠; 康英伟; 郑鹏远; 彭道刚

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DOI:10.3969/j.issn.1006-4729.2016.03.010

Journal of ShangHai University of Electric Power :2016,32(3):252-256,273

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基于遗传模拟退火算法的最大功率点跟踪研究

王亚楠^1,2, 康英伟^1,2, 郑鹏远^1,2, 彭道刚^1,2

(1.上海电力学院;2.上海发电过程智能管控工程技术研究中心)

Research of Maximum Power Point Tracking Based on Genetic Simulated Annealing Algorithm

WANG Yanan^1,2, KANG Yingwei^1,2, ZHENG Pengyuan^1,2, PENG Daogang^1,2

(1.Shanghai University of Electric Power, Shanghai 200090, China;2.Shanghai Engineering Research Center of Intelligence Management and Control for Power Process, Shanghai 200090, China)

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Received:September 29, 2015

中文摘要: 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.

中文关键词: 光伏发电最大功率点跟踪遗传模拟退火算法

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.

keywords: photovoltaic power generation maximum power point tracking genetic simulated annealing algorithm

文章编号：20160310 中图分类号： 文献标志码：

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).

Author Name	Affiliation	E-mail
WANG Yanan	Shanghai University of Electric Power, Shanghai 200090, China Shanghai Engineering Research Center of Intelligence Management and Control for Power Process, Shanghai 200090, China	wangyanan1990@126.com
KANG Yingwei	Shanghai University of Electric Power, Shanghai 200090, China Shanghai Engineering Research Center of Intelligence Management and Control for Power Process, Shanghai 200090, China
ZHENG Pengyuan	Shanghai University of Electric Power, Shanghai 200090, China Shanghai Engineering Research Center of Intelligence Management and Control for Power Process, Shanghai 200090, China
PENG Daogang	Shanghai University of Electric Power, Shanghai 200090, China Shanghai Engineering Research Center of Intelligence Management and Control for Power Process, Shanghai 200090, China

Author Name	Affiliation	E-mail
WANG Yanan	Shanghai University of Electric Power, Shanghai 200090, China Shanghai Engineering Research Center of Intelligence Management and Control for Power Process, Shanghai 200090, China	wangyanan1990@126.com
KANG Yingwei	Shanghai University of Electric Power, Shanghai 200090, China Shanghai Engineering Research Center of Intelligence Management and Control for Power Process, Shanghai 200090, China
ZHENG Pengyuan	Shanghai University of Electric Power, Shanghai 200090, China Shanghai Engineering Research Center of Intelligence Management and Control for Power Process, Shanghai 200090, China
PENG Daogang	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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