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Appl. Sci. 2018, 8(4), 558; doi:10.3390/app8040558

An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading

Department of Energy, Politecnico di Milano, via La Masa 34, 20156 Milano, Italy
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Received: 8 March 2018 / Revised: 28 March 2018 / Accepted: 29 March 2018 / Published: 4 April 2018
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
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Abstract

The increase of renewable energy usage in the last two decades, in particular photovoltaic (PV) systems, has opened up different solar plant configurations that need to operate and properly perform in terms of efficient power transfer with respect to all of the involved components, such as inverters, grid interface, storage, and other electrical loads. In such applications, the power characteristics of the plant modules all together represent the main components that are responsible for power extraction, depending on both external and internal factors. Conventional maximum power point tracking techniques may not have a proper conversion efficiency under particular external dynamic conditions. This paper proposes an evolutionary-based maximum power point tracking algorithm suitable to operate under dynamic partial shading conditions and compares its performance with classical maximum power point tracking methods in order to evaluate their conversion efficiency in partial shading scenarios with relevant and dynamic changes in the environmental conditions. Simulations taking into account the different dynamic shading conditions were carried out to prove the effectiveness and limitations of the proposed approach with respect to classical algorithms. View Full-Text
Keywords: photovoltaics; MPPT algorithm; evolutionary algorithms; particle swarm optimization photovoltaics; MPPT algorithm; evolutionary algorithms; particle swarm optimization
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Dolara, A.; Grimaccia, F.; Mussetta, M.; Ogliari, E.; Leva, S. An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading. Appl. Sci. 2018, 8, 558.

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