Open AccessGrade point average assessment for metaheuristic GMPP techniques of partial shaded PV systems
Author(s) -
Eltamaly Ali M.,
Farh Hassan M.H.,
AlSaud Mamdooh S.
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2018.5336
Subject(s) - maximum power point tracking , photovoltaic system , maximum power principle , computer science , ranking (information retrieval) , point (geometry) , metaheuristic , shading , energy (signal processing) , power (physics) , mathematical optimization , artificial intelligence , mathematics , engineering , statistics , physics , geometry , computer graphics (images) , quantum mechanics , inverter , electrical engineering
Photovoltaic (PV) maximum power point tracker (MPPT) is compulsory in PV systems to improve its output power and efficiency. Conventional techniques can track the maximum power under uniform irradiances efficiently and accurately. Nevertheless, in case of partial shading conditions where multiple peaks are generated, these conventional techniques may stick at any local peak of the P – V curve of the PV energy systems. Metaheuristic techniques have been applied to PV energy systems to overcome this limitation, where most of these techniques can catch the global MPP (GMPP) easily and efficiently. In case of dynamic change of partial shading, most of these techniques need reinitialisation to disperse the search agents to look again for a new position and value of GMPP. This study introduces a brief description, assessment and evaluation for these techniques. In addition, it proposes a novel assessment criterion based on grade point average for evaluating and ranking the 20 famous metaheuristic and hybrid GMPP techniques. This evaluation methodology can help researchers, designer and decision maker to choose the best option for MPPT of dynamic change of partially shading PV energy systems.