Parameter Extraction of Solar Photovoltaic Models Using Enhanced Levy Flight Based Grasshopper Optimization Algorithm
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ORIGINAL ARTICLE
Parameter Extraction of Solar Photovoltaic Models Using Enhanced Levy Flight Based Grasshopper Optimization Algorithm Diab Mokeddem1 Received: 4 May 2020 / Revised: 10 July 2020 / Accepted: 16 October 2020 © The Korean Institute of Electrical Engineers 2020
Abstract Recently, solar photovoltaic (PV) systems are becoming the tendency theme motivating researchers focus. The appropriate design of PV cells is an important task, challenged by the development of a useful model able to simulate the current vs voltage characteristics of the real solar cell and the accurate estimation of the PV cell’s parameter values. This paper proposes an improved Levy flight based grasshopper optimization algorithm (LGOA) to estimate the parameters of three PV models, i.e., single diode, double diode, and PV module. The incorporation of Levy flight trajectory to the basic grasshopper optimization algorithm (GOA), ensure solutions diversity and enhances the exploration and exploitation capabilities as well. To further validate its effectiveness LGOA is applied to the Sharp ND-R250A5 module under different operating conditions of irradiance and temperature. Experimental results demonstrate that LGOA has the ability to extract the parameters of PV models with high performance and good accuracy compared to the standard GOA. Keywords Photovoltaic · Estimation · Levy flight · Grasshopper optimization algorithm
1 Introduction Renewable energy is increasingly conquering different fields of human life as a promising alternative of fossil fuels. In global power systems, solar energy has motivated researchers, being inexhaustible, available, environmentally friendly, and easy to install and maintain [1]. More precisely, photovoltaic (PV) cells have a significant role in converting solar radiance to electrical energy. A good performance of photovoltaic cells requires an appropriate mathematical modelling that simulates the PV cell’s behaviour and accurately estimates its parameter’s optimal values. The most common and widely adopted mathematical models are the single diode (SD) [2] and the double diode (DD) [3]. The problem of estimating the PV parameters is nonlinear and multimodal, and can be defined as an optimization problem. Among the metaheuristic techniques employed to determine the parameters of photovoltaic cells we can mention: Guaranteed convergence particle swarm optimization * Diab Mokeddem [email protected] 1
Department of Electrical Engineering, Faculty of Technology, University of Ferhat Abbas Setif-1, Setif, Algeria
(GCPSO) [2], improved chaotic whale optimization algorithm (ICWOA) [4], artificial bee colony (ABC) [5], Backtracking search algorithm with Levy flight (LFBSA) [6], evaporation rate based water cycle algorithm (ER-WCA) [7] and self-adaptive teaching learning based optimization (SATLBO) [8]. In the context of efficient metaheuristic methods, the grasshopper optimization algorithm (GOA) is recently proposed in 2017 by Saremi et al. [9]. However, several improvement strategies are introduced in
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