4.7 Review

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

Journal

JOURNAL OF CLEANER PRODUCTION
Volume 268, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2020.121983

Keywords

MPPT; PV systems; Partial shading condition; Review

Funding

  1. National Natural Science Foundation of China [61963020, 51907112, 51777078, 51977102]
  2. Natural Science Foundation of Guangzhou Province of China [2019A1515011671]
  3. Fundamental Research Funds for the Central Universities [D2172920]
  4. Key Projects of Basic Research and Applied Basic Research in Universities of Guangdong Province [2018KZDXM001]
  5. Science and Technology Projects of China Southern Power Grid [GDKJXM20172831]

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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. (c) 2020 Elsevier Ltd. All rights reserved.

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