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A PSO‐based maximum power point tracking for photovoltaic systems under environmental and partially shaded conditions
Author(s) -
Sarvi Mohammad,
Ahmadi Saeedeh,
Abdi Shirzad
Publication year - 2015
Publication title -
progress in photovoltaics: research and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.286
H-Index - 131
eISSN - 1099-159X
pISSN - 1062-7995
DOI - 10.1002/pip.2416
Subject(s) - maximum power point tracking , photovoltaic system , maximum power principle , control theory (sociology) , particle swarm optimization , power (physics) , solar irradiance , irradiance , tracking (education) , computer science , point (geometry) , mathematics , engineering , algorithm , physics , meteorology , electrical engineering , optics , artificial intelligence , psychology , quantum mechanics , inverter , pedagogy , geometry , control (management)
To increase the efficiency of photovoltaic (PV) systems, maximum power point (MPP) tracking of the solar arrays is needed. Solar arrays output power depends on the solar irradiance and temperature. Also the mismatch phenomenon caused by partial shade will affect the output power of solar systems and lead to the incorrect operation of conventional MPP tracker. Under partially shaded conditions, the solar array power–current characteristic has multiple maximum. This paper presents a maximum power point tracking (MPPT) with particle swarm optimization method for PV systems under partially shaded condition. The performance of the proposed method is compared with perturb and observe (P&O), improved P&O, voltage‐based maximum power point tracking and current‐based maximum power point tracking algorithms, especially, under partially shaded condition. Simulation results confirm that proposed MPPT algorithm with high accuracy can track the peak power point under different insolation, temperature and partially shaded conditions, and it has the best performance in comparison with four mentioned MPPT algorithms. Also under rapidly changing atmospheric conditions, the P&O algorithm is diverged. Copyright © 2013 John Wiley & Sons, Ltd.