Comprehensive overview of maximum power point tracking algorithms of PV systems under partial shading condition

Comprehensive overview of maximum power point tracking algorithms of PV systems under partial shading condition
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局部遮挡条件下光伏系统最大功率点跟踪算法全面综述

DOI:
10.1016/j.jclepro.2020.121983
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发表时间:
2020-09
影响因子:
11.1
通讯作者:
Sun Liming
Sun Liming
中科院分区:
环境科学与生态学1区
文献类型:
--
作者:
Yang Bo;Zhu Tianjiao;Wang Jingbo;Shu Hongchun;Yu Tao;Zhang Xiaoshun;Yao Wei;Sun Liming

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本文旨在对部分遮阳条件下光伏(PV)系统最大功率点跟踪(MPPT)方法进行全面综述。特别是各种MPPT控制方法的开发和利用,对于保证光伏系统可靠、高效地提取最大功率具有重要意义。因此,本文系统地总结和讨论了PSC下光伏系统中使用的各种MPPT算法,共阐述了62种MPPT算法及其修改。并将其分为常规算法、元启发式算法、混合算法、基于数学的算法、人工智能算法、基于特征曲线挖掘的算法和其他算法等7类。特别地,有25种元启发式算法进一步分为三类进行更详细的讨论,即基于生物学的算法,基于物理学的算法和基于社会学的算法。一般情况下,读卡器可以根据应用需求和系统规格做出最合适的选择。本综述可作为相关领域进一步研究的一站式手册。
This paper is designed to undertake a comprehensive review on state-of-the-art maximum power point tracking (MPPT) methods of photovoltaic (PV) systems under partial shading condition (PSC). Particularly, the exploitation and utilization of various MPPT control approaches are of great significance to ensure a reliable and efficient maximum power extracting of PV systems. Hence, this paper systematically summarizes and discusses various MPPT algorithms utilized in PV systems under PSC, in which a total of 62 MPPT algorithms are elaborated, together with their modifications. Besides, they are categorized into seven groups, e.g., conventional algorithms, meta-heuristic algorithms, hybrid algorithms, mathematics-based algorithms, artificial intelligence (AI) algorithms, algorithms based on exploitation of characteristics curves, and other algorithms. Particularly, there are 25 meta-heuristic algorithms further divided into three categories for a more detailed discussion, namely, biology-based algorithms, physics-based algorithms, and sociology-based algorithms. In general, readers can make the most suitable choices according to application requirements and system specifications. This review can be regarded as a one-stop handbook for further studies in related field.
基于动态领导者的集体智能,用于跟踪受部分阴影条件影响的光伏系统的最大功率点
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