A Stochastic Optimization Algorithm to Enhance Controllers of Photovoltaic Systems

Samia Charfeddine, Hadeel Alharbi, Houssem Jerbi, Mourad Kchaou, Rabeh Abbassi and Víctor Leiva
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Samia Charfeddine: Research Unit of Photovoltaic, Wind and Geothermal Systems, National Engineering School of Gabès, University of Gabès, Gabès 6029, Tunisia
Hadeel Alharbi: Department of Computer Science, College of Computer Science and Engineering, University of Hail, Hail 1234, Saudi Arabia
Houssem Jerbi: Department of Industrial Engineering, College of Engineering, University of Hail, Hail 1234, Saudi Arabia
Mourad Kchaou: Department of Electrical Engineering, College of Engineering, University of Hail, Hail 1234, Saudi Arabia
Rabeh Abbassi: Department of Electrical Engineering, College of Engineering, University of Hail, Hail 1234, Saudi Arabia
Víctor Leiva: School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile

Mathematics, 2022, vol. 10, issue 12, 1-26

Abstract: Increasing energy needs, pollution of nature, and eventual depletion of resources have prompted humanity to obtain new technologies and produce energy using clean sources and renewables. In this paper, we design an advanced method to improve the performance of a sliding mode controller combined with control theory for a photovoltaic system. Specifically, we decouple the controlled output of the system from any perturbation source and assess the effectiveness of the results in terms of solution quality, closed-loop control stability, and dynamical convergence of the state variables. This study focuses on the climatic conditions that may affect the behavior of a solar energy plant to supply a motor with the highest possible efficiency and nominal operating conditions. The designed method enables us to obtain an optimal performance by means of advanced control techniques and a slime mould stochastic optimization algorithm. The efficiency and performance of this method are examined based on a benchmark model of a photovoltaic system via numerical analysis and simulation.

Keywords: control theory; feedback linearization; metaheuristic optimization; numerical analysis; perturbations; simulations; solar energy; state variables; stochasticity (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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