Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system
Abstract This paper suggests an optimal maximum power point tracking (MPPT) control scheme for a grid-connected photovoltaic (PV) system using the arithmetic optimization algorithm (AOA). The parameters of the proportional-integral (PI) controller-based incremental conductance (IC) MPPT are optimall...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-32793-0 |
| _version_ | 1797846056169373696 |
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| author | Mohamed Ahmed Ebrahim Mohamed Shymaa Nasser Ahmed Mohamed Eladly Metwally |
| author_facet | Mohamed Ahmed Ebrahim Mohamed Shymaa Nasser Ahmed Mohamed Eladly Metwally |
| author_sort | Mohamed Ahmed Ebrahim Mohamed |
| collection | DOAJ |
| description | Abstract This paper suggests an optimal maximum power point tracking (MPPT) control scheme for a grid-connected photovoltaic (PV) system using the arithmetic optimization algorithm (AOA). The parameters of the proportional-integral (PI) controller-based incremental conductance (IC) MPPT are optimally selected using AOA. To accomplish this study, a 100-kW benchmark PV system connected to a medium distribution utility is constructed and analyzed employing MATLAB/SIMULINK. The optimization framework seeks to minimize four standard benchmark performance indices, then select the best of the best among them. To verify the efficacy of the recommended methodology, a comprehensive comparison is conducted between AOA-based PI-IC-MPPT, modified incremental conductance MPPT (MIC), grey wolf optimization (GWO), genetic algorithm (GA), and particle swarm optimization (PSO)-based MPPT. The proposed control approach has achieved a reduction of 61, 3, 4.5, and 26.9% in the rise time and a decrease of 94, 84.7, 86.6, and 79.3% in the settling time compared with MIC, GWO, GA, and PSO in extracting MPPT of the proposed system, respectively. |
| first_indexed | 2024-04-09T17:48:50Z |
| format | Article |
| id | doaj.art-fe6d75dcd9874b3e9456e55c992d6c6d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T17:48:50Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-fe6d75dcd9874b3e9456e55c992d6c6d2023-04-16T11:12:41ZengNature PortfolioScientific Reports2045-23222023-04-0113111910.1038/s41598-023-32793-0Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic systemMohamed Ahmed Ebrahim Mohamed0Shymaa Nasser Ahmed1Mohamed Eladly Metwally2Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha UniversityDepartment of Electrical Engineering, El Shorouk High Institute of EngineeringDepartment of Electrical Engineering, El Shorouk High Institute of EngineeringAbstract This paper suggests an optimal maximum power point tracking (MPPT) control scheme for a grid-connected photovoltaic (PV) system using the arithmetic optimization algorithm (AOA). The parameters of the proportional-integral (PI) controller-based incremental conductance (IC) MPPT are optimally selected using AOA. To accomplish this study, a 100-kW benchmark PV system connected to a medium distribution utility is constructed and analyzed employing MATLAB/SIMULINK. The optimization framework seeks to minimize four standard benchmark performance indices, then select the best of the best among them. To verify the efficacy of the recommended methodology, a comprehensive comparison is conducted between AOA-based PI-IC-MPPT, modified incremental conductance MPPT (MIC), grey wolf optimization (GWO), genetic algorithm (GA), and particle swarm optimization (PSO)-based MPPT. The proposed control approach has achieved a reduction of 61, 3, 4.5, and 26.9% in the rise time and a decrease of 94, 84.7, 86.6, and 79.3% in the settling time compared with MIC, GWO, GA, and PSO in extracting MPPT of the proposed system, respectively.https://doi.org/10.1038/s41598-023-32793-0 |
| spellingShingle | Mohamed Ahmed Ebrahim Mohamed Shymaa Nasser Ahmed Mohamed Eladly Metwally Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system Scientific Reports |
| title | Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system |
| title_full | Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system |
| title_fullStr | Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system |
| title_full_unstemmed | Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system |
| title_short | Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system |
| title_sort | arithmetic optimization algorithm based maximum power point tracking for grid connected photovoltaic system |
| url | https://doi.org/10.1038/s41598-023-32793-0 |
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