Artificial intelligence and bio-inspired soft computing-based maximum power plant tracking for a solar photovoltaic system under non-uniform solar irradiance shading conditions - a review

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dc.contributor.authorAli, Amjad
dc.contributor.authorIrshad, Kashif
dc.contributor.authorKhan, Mohammad Farhan
dc.contributor.authorHossain, Md Moinul
dc.contributor.authorAl-Duais, Ibrahim N. A.
dc.contributor.authorMalik, Muhammad Zeeshan
dc.date.accessioned2021-10-06T09:16:25Z
dc.date.available2021-10-06T09:16:25Z
dc.date.issued2021-09-23
dc.description.abstractSubstantial progress in solar photovoltaic (SPV) dissemination in grid-connected and standalone power generation systems has been witnessed during the last two decades. However, weather intermittency has a non-linear characteristic impact on solar photovoltaic output, which can cause considerable loss in the system’s overall output. To overcome these inevitable losses and optimize the SPV output, maximum power point tracking (MPPT) is mounted in the middle of the power electronics converters and SPV to achieve the maximum output with better precision from the SPV system under intermittent weather conditions. As MPPT is considered an essential part of the SPV system, up to now, many researchers have developed numerous MPPT techniques, each with unique features. A Google Scholar survey from 2015–2021 was performed to scrutinize the number of published review papers in this area. An online search established that on different MPPT techniques, overall, 100 review articles were published; out of these 100, seven reviews on conventional MPPT techniques under shading or partial shading and only four under non-uniform solar irradiance are published. Unfortunately, no dedicated review article has explicitly focused on soft computing MPPT (SC-MPPT) techniques. Therefore, a comprehensive review of articles on SC-MPPT techniques is desirable, in which almost all the familiar SC-MPPT techniques have to be summarized in one piece. This review article concentrates explicitly on soft computing-based MPPT techniques under non-uniform irradiance conditions along with their operating principles, block/flow diagram. It will not only be helpful for academics and researchers to provide a future direction in SC-MPPT optimization research, but also help the field engineers to select the appropriate SC-MPPT for SPV according to system design and environmental conditions.en_UK
dc.identifier.citationAli A, Irshad K, Khan MF, et al., (2021) Artificial intelligence and bio-inspired soft computing-based maximum power plant tracking for a solar photovoltaic system under non-uniform solar irradiance shading conditions - a review. Sustainability, Volume 13, Issue 9, Article number 10575en_UK
dc.identifier.issn2071-1050
dc.identifier.urihttps://doi.org/10.3390/su131910575
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/17144
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectmaximum power point tracking (MPPT)en_UK
dc.subjectsoft computingen_UK
dc.subjectsolar photovoltaic (SPV)en_UK
dc.subjectnon-uniform solar irradianceen_UK
dc.titleArtificial intelligence and bio-inspired soft computing-based maximum power plant tracking for a solar photovoltaic system under non-uniform solar irradiance shading conditions - a reviewen_UK
dc.typeArticleen_UK

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