International Journal of Power Electronics and Drive Systems (IJPEDS) Vol. 13, No. 2, June 2022, pp. 1238~1245 ISSN: 2088-8694, DOI: 10.11591/ijpeds.v13.i2.pp1238-1245  1238 Journal homepage: http://ijpeds.iaescore.com GMPPT approach for photovoltaic systems under partial shading conditions using a genetic algorithm Tahar Tafticht, Mouctar Tchakala, Md Jahidur Rahman School of Engineering, University of Québec in Abitibi-Témiscamingue (UQAT), Rouyn-Noranda, Canada Article Info ABSTRACT Article history: Received Feb 23, 2022 Revised Mar 30, 2022 Accepted Apr 20, 2022 In this paper a new global maximum power point tracking (GMPPT) approach is proposed for a photovoltaic (PV) module using a genetic algorithm (GA) system under partial shading conditions. The partial shading condition is one of the adverse phenomena that the PV system experiences, it is difficult to track true peaks because of existence of multiple peaks in a PV array (partially shaded based). The conventional GMPPT algorithms met in the literature, the initial value of reference is approximated arbitrarily for various conditions of irradiation and temperature and they can’t distinguish between the local and global peaks if partial shading occurs, what reduces the performances of the tracking of the point of optimal operation of PV systems. So, to improve these performances, we proposed a nonlinear based GMPPT method to estimate the initial optimal operating point. To compare with conventional GAs methods, the global peak (GP) tracking process is accomplished after follow the far fewer power perturbation steps. This approach made a possible to largely improve the GP tracking in the PV system, improve the accuracy of the GMPPT algorithms, and accelerate the convergence speed. This GMPPT method is checked for different shading profiles through the simulation and verification. Keywords: Genetic algorithm Global maximum power point tracking Partial shading Solar PV systems This is an open access article under the CC BY-SA license. Corresponding Author: Tahar Tafticht School of Engineering, University of Québec in Abitibi-Témiscamingue (UQAT) 445 Boulevard de l’Université, Rouyn-Noranda, QC J9X 5E4, Canada Email: tahar.tafticht@uqat.ca 1. INTRODUCTION Under the conditions of partially shading, the current pass throughout a photovoltaic (PV) module is inadequated due to nonuniform irradiation. The current generated from its highest shaded module helps to reduce the output power of the overall PV array [1]. Furthermore, during partial shading conditions the power-voltage characteristic of PV arrays exhibits multiple local of maximum power points and making it difficult to find the GP using traditional tracking methods [2]. In literature, several methods and algorithms have been reported for a global maximum power point tracking (GMPPT). Different conventional GMPPT algorithms are modified hill-climbing perturbation and observation (P&O) method, incremental-conductance method, fractional short circuit current method, fractional open-circuit voltage method. These techniques have advantages of their simplicity. However, due to the failure to track exact GP under partial shading conditions and trap at local maximum power points, modifications are required [3]. For accurate GP tracking which increases the efficiency of the PV systems, other authors proposed several GMPPT techniques to track GP under partial shading conditions by using evolutionary techniques and artificial intelligence techniques like fuzzy, artificial neural network (ANN), particle swarm optimization (PSO) and, genetic algorithm (GA) [4]-[6]. These techniques are able to track the GP when the PV system characteristics exhibit a single MPP. However, the