A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading

This article presents a novel MPPT (maximum power point tracking) algorithm, based on a modified GA (genetic algorithm). When photovoltaic systems are affected by partial shading, a GMPPT (global maximum power point tracking) algorithm is required to increase the energy harvesting capability of the...

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Vydané v:Energy (Oxford) Ročník 74; s. 374 - 388
Hlavní autori: Daraban, Stefan, Petreus, Dorin, Morel, Cristina
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Kidlington Elsevier Ltd 01.09.2014
Elsevier
Predmet:
Algorithms
Applied sciences
Automatic
Computer simulation
DSP (digital signal processor)
Energy
Engineering Sciences
Equipments, installations and applications
Exact sciences and technology
genes
Genetic algorithm
Genetic algorithms
GMPP (global maximum power point)
Mathematical models
Maximum power
MPPT (maximum power point tracking)
Natural energy
Partial shading
Photovoltaic cells
Photovoltaic conversion
Photovoltaic system
population size
shade
Solar cells
solar collectors
Solar energy
Tracking
MPPT (maximum power point tracking)
Partial shading
Genetic algorithm
DSP (digital signal processor)
Photovoltaic system
GMPP (global maximum power point)
ISSN:0360-5442, 1873-6785
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Shrnutí:This article presents a novel MPPT (maximum power point tracking) algorithm, based on a modified GA (genetic algorithm). When photovoltaic systems are affected by partial shading, a GMPPT (global maximum power point tracking) algorithm is required to increase the energy harvesting capability of the system. A new GMPPT algorithm is proposed in this article: a P&O (perturb and observe) algorithm is integrated inside the GA function and creates a single algorithm. By embedding a simple MPPT algorithm (P&O) inside the structure of the GA, the population size and the number of iterations are decreased, thus finding the MPP (maximum power point) in a shorter time. The algorithm parameters (population size, number of genes, and number of iterations) are optimized and the final solution is provided. A macromodel is used to average the real DC–DC converter and reduce the computation burden of the simulator, thus reducing the simulation time. The control part and the GMPPT algorithm were implemented on a DSP (digital signal processor) and tested on an experimental small scale photovoltaic system. A description of this algorithm and its performances are detailed in this article, verified through simulation and experimental results. •A novel global maximum power point algorithm based on a modified GA (genetic algorithm) is proposed.•A simple P&O (perturb and observe) is embedded inside the structure of the genetic algorithm.•A method to optimize the proposed GA according to the used hardware is presented.•The proposed algorithm is compared with multiple GMPPT (global maximum power point tracking) algorithms.•The proposed algorithm is tested on an experimental prototype.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2014.07.001