Identification of PV solar cells and modules parameters using the genetic algorithms: Application to maximum power extraction

By M. Zagrouba, Anis Sellami, M. Bouaïcha, M. Ksouri. 2010

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A Method for the Analytical Extraction of the Single-Diode PV Model Parameters (2016, methodology): “ting technique is applied to measured data in [26]–[29], while various metaheuristic algorithms have been proposed in the literature, implementing genetic algorithms [30], neural net-”
A New Parameter Estimation Method of Solar Photovoltaic (2018, methodology): “Additionally, in order to resolve the issues, as existing in numerical and analytical methods, several metaheuristic algorithms (MAs)-based approaches, such as, genetic algorithm [12], particle swarm algorithm [13], [14], simulated annealing (SA) algorithm [15], differential evolution algorithm [16]”
Parameter extraction of solar cell models using repaired adaptive differential evolution (2013, background): “, 2011), such as genetic algorithms (GAs) (Jervase et al., 2001; Zagrouba et al., 2010), particle swarm optimization (PSO) (Ye et al.”
Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules (2021, background): “For example, genetic algorithms (GA) (Zagrouba et al., 2010), particle swarm optimization (PSO) (Chopde et al., 2016), differential evolution (DE) (Biswas et al., 2019), bacterial foraging algorithm (BFA) (Rajasekar et al., 2013), slime mold algorithm (SMA)1 (Kumar et al., 2020), sunflower…”