Dwarf Mongoose Optimizer for Optimal Modeling of Solar PV Systems and Parameter Extraction Article Swipe
Ghareeb Moustafa
,
Idris H. Smaili
,
Dhaifallah R. Almalawi
,
Ahmed R. Ginidi
,
Abdullah M. Shaheen
,
Mostafa Elshahed
,
Hany S. E. Mansour
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12244990
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12244990
This article presents a modified intelligent metaheuristic form of the Dwarf Mongoose Optimizer (MDMO) for optimal modeling and parameter extraction of solar photovoltaic (SPV) systems. The foraging manner of the dwarf mongoose animals (DMAs) motivated the DMO’s primary design. It makes use of distinct DMA societal groups, including the alpha category, scouts, and babysitters. The alpha female initiates foraging and chooses the foraging path, bedding places, and distance travelled for the group. The newly presented MDMO has an extra alpha-directed knowledge-gaining strategy to increase searching expertise, and its modifying approach has been led to some extent by the amended alpha. For two diverse SPV modules, Kyocera KC200GT and R.T.C. France SPV modules, the proposed MDMO is used as opposed to the DMO to efficiently estimate SPV characteristics. By employing the MDMO technique, the simulation results improve the electrical characteristics of SPV systems. The minimization of the root mean square error value (RMSE) has been used to compare the efficiency of the proposed algorithm and other reported methods. Based on that, the proposed MDMO outperforms the standard DMO. In terms of average efficiency, the MDMO outperforms the standard DMO approach for the KC200GT module by 91.7%, 84.63%, and 75.7% for the single-, double-, and triple-diode versions, respectively. The employed MDMO technique for the R.T.C France SPV system has success rates of 100%, 96.67%, and 66.67%, while the DMO’s success rates are 6.67%, 10%, and 0% for the single-, double-, and triple-diode models, respectively.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics12244990
- https://www.mdpi.com/2079-9292/12/24/4990/pdf?version=1702451990
- OA Status
- gold
- Cited By
- 21
- References
- 75
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389675530
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389675530Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/electronics12244990Digital Object Identifier
- Title
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Dwarf Mongoose Optimizer for Optimal Modeling of Solar PV Systems and Parameter ExtractionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-12-13Full publication date if available
- Authors
-
Ghareeb Moustafa, Idris H. Smaili, Dhaifallah R. Almalawi, Ahmed R. Ginidi, Abdullah M. Shaheen, Mostafa Elshahed, Hany S. E. MansourList of authors in order
- Landing page
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https://doi.org/10.3390/electronics12244990Publisher landing page
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https://www.mdpi.com/2079-9292/12/24/4990/pdf?version=1702451990Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2079-9292/12/24/4990/pdf?version=1702451990Direct OA link when available
- Concepts
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Photovoltaic system, Mongoose, Foraging, Metaheuristic, Minification, Bat algorithm, Mean squared error, Computer science, Mathematical optimization, Simulation, Biological system, Mathematics, Biology, Algorithm, Statistics, Ecology, Particle swarm optimizationTop concepts (fields/topics) attached by OpenAlex
- Cited by
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21Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 14Per-year citation counts (last 5 years)
- References (count)
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75Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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