Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis
Abstract A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track pow...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-59776-z |
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| author | Shahjahan Alias Sarang Muhammad Amir Raza Madeeha Panhwar Malhar Khan Ghulam Abbas Ezzeddine Touti Abdullah Altamimi Andika Aji Wijaya |
| author_facet | Shahjahan Alias Sarang Muhammad Amir Raza Madeeha Panhwar Malhar Khan Ghulam Abbas Ezzeddine Touti Abdullah Altamimi Andika Aji Wijaya |
| author_sort | Shahjahan Alias Sarang |
| collection | DOAJ |
| description | Abstract A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems. |
| first_indexed | 2024-04-24T07:15:13Z |
| format | Article |
| id | doaj.art-73939b567742481ebfcd47533b2fbdc6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T07:15:13Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-73939b567742481ebfcd47533b2fbdc62024-04-21T11:19:23ZengNature PortfolioScientific Reports2045-23222024-04-0114111810.1038/s41598-024-59776-zMaximizing solar power generation through conventional and digital MPPT techniques: a comparative analysisShahjahan Alias Sarang0Muhammad Amir Raza1Madeeha Panhwar2Malhar Khan3Ghulam Abbas4Ezzeddine Touti5Abdullah Altamimi6Andika Aji Wijaya7Department of Electrical Engineering, Mehran University of Engineering and TechnologyDepartment of Electrical Engineering, Mehran University of Engineering and TechnologyDepartment of Electrical Engineering, Mehran University of Engineering and TechnologyDepartment of Electrical Engineering, Mehran University of Engineering and TechnologySchool of Electrical Engineering, Southeast UniversityDepartment of Electrical Engineering, College of Engineering, Northern Border UniversityDepartment of Electrical Engineering, College of Engineering, Majmaah UniversityMechanical Engineering Department, College of Engineering, University of Business and TechnologyAbstract A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems.https://doi.org/10.1038/s41598-024-59776-zConventional MPPTsArtificial intelligence MPPTsSolar energySustainability |
| spellingShingle | Shahjahan Alias Sarang Muhammad Amir Raza Madeeha Panhwar Malhar Khan Ghulam Abbas Ezzeddine Touti Abdullah Altamimi Andika Aji Wijaya Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis Scientific Reports Conventional MPPTs Artificial intelligence MPPTs Solar energy Sustainability |
| title | Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis |
| title_full | Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis |
| title_fullStr | Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis |
| title_full_unstemmed | Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis |
| title_short | Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis |
| title_sort | maximizing solar power generation through conventional and digital mppt techniques a comparative analysis |
| topic | Conventional MPPTs Artificial intelligence MPPTs Solar energy Sustainability |
| url | https://doi.org/10.1038/s41598-024-59776-z |
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