An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/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.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math11224565
- https://www.mdpi.com/2227-7390/11/22/4565/pdf?version=1699337480
- OA Status
- gold
- Cited By
- 39
- References
- 89
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388459504
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388459504Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/math11224565Digital Object Identifier
- Title
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An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell ModelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-07Full publication date if available
- Authors
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Rabeh Abbassi, Salem Saidi, Shabana Urooj, Bilal Naji Alhasnawi, Mohamad A. Alawad, M. PremkumarList of authors in order
- Landing page
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https://doi.org/10.3390/math11224565Publisher landing page
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https://www.mdpi.com/2227-7390/11/22/4565/pdf?version=1699337480Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/11/22/4565/pdf?version=1699337480Direct OA link when available
- Concepts
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Particle swarm optimization, Differential evolution, Metaheuristic, Computer science, Swarm behaviour, Photovoltaic system, Algorithm, Mathematical optimization, Mathematics, Artificial intelligence, Engineering, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
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39Total citation count in OpenAlex
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2025: 18, 2024: 21Per-year citation counts (last 5 years)
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89Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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