ANN based MPPT method for rapidly variable shading conditions

Applied Energy

Volumn 145, Issue , 2015, Pages 124-132

  Rizzo, Santi Agatino ^a   Scelba, Giacomo ^a  

^a UNIVERSITY OF CATANIA   (Italy)

Author keywords

Artificial neural networks; Electric vehicles; Maximum Power Point Tracking (MPPT); Photovoltaic (PV); Shading; Solar energy

Indexed keywords

COST EFFECTIVENESS; ELECTRIC VEHICLES; MAXIMUM POWER POINT TRACKERS; NEURAL NETWORKS; PHOTOVOLTAIC CELLS; SOLAR ENERGY; SOLAR POWER GENERATION;

MAXIMUM POWER POINT TRACKING; OPERATING CONDITION; PHOTOVOLTAIC; PHOTOVOLTAIC SYSTEM (PVS); POWER VOLTAGE CHARACTERISTICS; PREDICTION ACCURACY; SHADING; VARIABLE SHADING;

NUMERICAL METHODS;

ACCURACY ASSESSMENT; ALGORITHM; ARTIFICIAL NEURAL NETWORK; ELECTRIC VEHICLE; NUMERICAL MODEL; PHOTOVOLTAIC SYSTEM; PREDICTION; SHADING; SOLAR POWER;

EID: 84923582119     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2015.01.077     Document Type: Article

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