Open AccessMaximum power point tracking technique using artificial bee colony and hill climbing algorithms during mismatch insolation conditions on PV array
Author(s) -
Goud J Saikrishna,
R. Kalpana,
Singh Bhim,
Kumar Shailendra
Publication year - 2018
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.2018.5116
Subject(s) - maximum power principle , maximum power point tracking , photovoltaic system , hill climbing , matlab , computer science , algorithm , power (physics) , convergence (economics) , duty cycle , point (geometry) , control theory (sociology) , mathematics , engineering , artificial intelligence , electrical engineering , physics , geometry , control (management) , quantum mechanics , inverter , economic growth , economics , operating system
This study presents a single current sensor based hybrid maximum power point tracking method to track the global maximum power point (GMPP) of the photovoltaic (PV) array during the mismatch insolation conditions. This method combines the artificial bee colony (ABC) and hill climbing (HC) algorithms to track the GMPP of a PV array. The proposed method uses the HC algorithm to identify the occurrence of mismatch insolation conditions on PV array. During the mismatch insolation conditions, the proposed method scans the battery charging current ( I charge ) versus duty cycle ( D ) characteristics of the power electronic interface circuit to classify the type of shading pattern of P – V curve and also to identify the vicinity of the GMPP. Based on the kind of shading pattern of a P – V curve, the proposed method operates either ABC or HC algorithm to track the GMPP. To improve the convergence speed of the proposed method, the search space of the ABC algorithm is reduced. The proposed method is modelled and simulated in MATLAB software and its performance is validated experimentally for various mismatch insolation conditions.