| 引用本文: | 朱梓嘉,肖辉,赵帅旗,刘忠兵.基于并行组合进化算法的光伏阵列最大功率点追踪[J].电力系统保护与控制,2020,48(4):1-10.[点击复制] |
| ZHU Zijia,XIAO Hui,ZHAO Shuaiqi,LIU Zhongbing.Maximum power point tracking of photovoltaic array based on parallel combination evolutionary algorithm[J].Power System Protection and Control,2020,48(4):1-10[点击复制] |
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| 摘要: |
| 针对光伏阵列在局部遮阴时功率的多峰输出，提出了一种新型并行组合进化算法(GA-DE-PSO)，解决了传统采用的单一进化算法以及单一改进算法追踪最大功率时的不稳定性和精确性不足的缺点。该方法将所有的可行解个体随机分为两个子种群，并行采用向量的差分进化模式和染色体的遗传模式，产生新型个体和备选个体。再通过粒子群算法进行混合选择，得到更为有效的可行域的指导信息，从而更快速地收敛到最优点，实现最大功率点追踪。仿真结果表明，组合算法保留了三种进化算法的优点，具有较高的寻优精度与稳定性。 |
| 关键词: 局部遮阴 最大功率点追踪 粒子群算法 差分进化算法 遗传算法 全局寻优 |
| DOI：10.19783/j.cnki.pspc.190348 |
| 投稿时间：2019-03-28修订日期：2019-07-04 |
| 基金项目:国家自然科学基金项目资助(51708194);湖南省教育厅科学研究重点项目(18A120) |
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| Maximum power point tracking of photovoltaic array based on parallel combination evolutionary algorithm |
| ZHU Zijia,XIAO Hui,ZHAO Shuaiqi,LIU Zhongbing |
| (College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China;College of Civil Engineering, Hunan University, Changsha 410082, China) |
| Abstract: |
| Aiming at the multi-peak output of photovoltaic arrays in partial shading, this paper proposes a new parallel combination evolutionary algorithm (GA-DE-PSO), which solves the disadvantages of instability and lack of precision of traditional single evolutionary algorithm to track the maximum power. The method randomly divides all feasible solution individuals into two sub-populations, and uses the differential evolution model of vectors and the genetic model of chromosomes to generate new individuals and candidate individuals. Then through the particle swarm algorithm for hybrid selection, the guidance information of the more effective feasible domain is obtained, so as to converge to the best advantage more quickly and achieve maximum power point tracking. The simulation results show that the combination algorithm retains the advantages of three evolutionary algorithms and has high precision and stability. This work is supported by National Natural Science Foundation of China (No. 51708194) and Key Research Project of Hunan Provincial Department of Education (No. 18A120). |
| Key words: partial shadow maximum power point tracking particle swarm optimization differential evolution algorithm genetic algorithm global optimization |
