An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

• Published in: Mathematics (Basel) 2023-11, Vol.11 (22), p.4565
• Main Authors: Abbassi, Rabeh, Saidi, Salem, Urooj, Shabana, Alhasnawi, Bilal Naji, Alawad, Mohamad A., Premkumar, Manoharan
• Format: Article
• Language: English
• Subjects: Accuracy; Air pollution; Alternative energy sources; Artificial intelligence; Circuit components; Convergence; Current voltage characteristics; double-diode; Emissions; Evolutionary algorithms; Evolutionary computation; Exploitation; Greenhouse gases; Heuristic methods; Internet of Things; Machine learning; Mathematical models; metaheuristic algorithm; Mountains; Optimization algorithms; Parameter estimation; Parameter identification; Particle swarm optimization; Photovoltaic cells; Power; Renewable resources; Search algorithms; single-diode; Solar energy; Solar energy industry; Swarm intelligence
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11224565

Accurate parameter estimation is crucial and challenging for the design and modeling of PV cells/modules. However, the high degree of non-linearity of the typical I–V characteristic further complicates this task. Consequently, significant research interest has been generated in recent years. Currently, this trend has been marked by a noteworthy acceleration, mainly due to the rise of swarm intelligence and the rapid progress of computer technology. This paper proposes a developed Mountain Gazelle Optimizer (MGO) to generate the best values of the unknown parameters of PV generation units. The MGO mimics the social life and hierarchy of mountain gazelles in the wild. The MGO was compared with well-recognized recent algorithms, which were the Grey Wolf Optimizer (GWO), the Squirrel Search Algorithm (SSA), the Differential Evolution (DE) algorithm, the Bat–Artificial Bee Colony Optimizer (BABCO), the Bat Algorithm (BA), Multiswarm Spiral Leader Particle Swarm Optimization (M-SLPSO), the Guaranteed Convergence Particle Swarm Optimization algorithm (GCPSO), Triple-Phase Teaching–Learning-Based Optimization (TPTLBO), the Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO), the quasi-Opposition-Based Learning Whale Optimization Algorithm (OBLWOA), and the Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO). The experimental findings and statistical studies proved that the MGO outperformed the competing techniques in identifying the parameters of the Single-Diode Model (SDM) and the Double-Diode Model (DDM) PV models of Photowatt-PWP201 (polycrystalline) and STM6-40/36 (monocrystalline). The RMSEs of the MGO on the SDM and the DDM of Photowatt-PWP201 and STM6-40/36 were 2.042717 ×10−3, 1.387641 ×10−3, 1.719946 ×10−3, and 1.686104 ×10−3, respectively. Overall, the identified results highlighted that the MGO-based approach featured a fast processing time and steady convergence while retaining a high level of accuracy in the achieved solution.