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

Stefan Daraban, Dorin Petreus and Cristina Morel

Energy, 2014, vol. 74, issue C, 374-388

Abstract: 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.

Keywords: MPPT (maximum power point tracking); Genetic algorithm; Photovoltaic system; Partial shading; DSP (digital signal processor); GMPP (global maximum power point) (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (58)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:74:y:2014:i:c:p:374-388

DOI: 10.1016/j.energy.2014.07.001

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