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自适应果蝇算法的多峰值光伏阵列最大功率点跟踪
Multi-peak PV array maximum power point tracking with Adaptive Fruit Fly Optimization Algorithm
投稿时间:2018-06-07  修订日期:2018-09-07
DOI:
中文关键词:  遮挡  最大功率点跟踪  自适应果蝇算法
英文关键词:shadow  MPPT  AFOA
基金项目:辽宁省教育厅青年项目
作者单位E-mail
原琳 辽宁工业大学电气工程学院 zhenjiangyuanlin@126.com 
程海军 辽宁工业大学电气工程学院 dlmchj@126.com 
赵凤贤 辽宁工业大学电气工程学院 306976325@qq.com 
摘要点击次数: 243
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中文摘要:
      实际应用中,光伏阵列(photovoltaic,简称PV)常存在遮挡现象,遮挡情况下的PV输出曲线呈多峰特性,与光照均匀时的单峰特性不同。此时,常规最大功率点跟踪方法大多以寻找到第一个峰值点而停止搜索,易造成陷入光伏阵列局部极值点的跟踪而失效。本文提出一种基于自适应的果蝇优化算法(Adaptive Fruit Fly Optimization Algorithm,简称AFOA),对原有果蝇算法的初始值设定及寻优步长进行改进,并定时与扰动观察法(Perturb & Observe,简称P&O)相结合,增强寻优算法的实时性。通过Matlab仿真,分别在光照均匀和遮挡情况下,与扰动观察法和粒子群优化算法(Particle Swarm Optimization,简称PSO)的跟踪效果进行了比较,仿真结果表明,无论有无遮挡现象,AFOA算法都可准确跟踪到系统的全局最大功率点,提高了系统的发电效率及输出功率的稳定性。
英文摘要:
      In practical applications, there is often a shaded phenomenon for the photovoltaic array (PV), and the PV output curve in the shaded situation has a multi-peak characteristic, which is different from that in the uniform illumination. At this point, most of the conventional maximum power point tracking method due to fall into the local extreme point of the track and failure. In this paper, an Adaptive Fruit Fly Optimization Algorithm (AFOA) is proposed, to improve the original algorithm of the initial value and the optimal step size settings. Combine with Perturb & Observe(P&O) to improve the real-time capability. The simulation results show that the IFOA algorithm is accurate with or without occlusion, which is compared with P & O and Particle Swarm Optimization (PSO) under the illumination uniformity and occlusion respectively. Tracking to the system's maximum power point, improve the system's power generation efficiency and output power stability.
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