RETRACTED ARTICLE: Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic Algorithm-optimized gains
Abstract As the significance and complexity of solar panel performance, particularly at their maximum power point (MPP), continue to grow, there is a demand for improved monitoring systems. The presence of variable weather conditions in Maroua, including potential partial shadowing caused by cloud c...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-53721-w |
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| author | Serge Raoul Dzonde Naoussi Kenfack Tsobze Saatong Reagan Jean Jacques Molu Wulfran Fendzi Mbasso Mohit Bajaj Mohamed Louzazni Milkias Berhanu Salah Kamel |
| author_facet | Serge Raoul Dzonde Naoussi Kenfack Tsobze Saatong Reagan Jean Jacques Molu Wulfran Fendzi Mbasso Mohit Bajaj Mohamed Louzazni Milkias Berhanu Salah Kamel |
| author_sort | Serge Raoul Dzonde Naoussi |
| collection | DOAJ |
| description | Abstract As the significance and complexity of solar panel performance, particularly at their maximum power point (MPP), continue to grow, there is a demand for improved monitoring systems. The presence of variable weather conditions in Maroua, including potential partial shadowing caused by cloud cover or urban buildings, poses challenges to the efficiency of solar systems. This study introduces a new approach to tracking the Global Maximum Power Point (GMPP) in photovoltaic systems within the context of solar research conducted in Cameroon. The system utilizes Genetic Algorithm (GA) and Backstepping Controller (BSC) methodologies. The Backstepping Controller (BSC) dynamically adjusts the duty cycle of the Single Ended Primary Inductor Converter (SEPIC) to align with the reference voltage of the Genetic Algorithm (GA) in Maroua’s dynamic environment. This environment, characterized by intermittent sunlight and the impact of local factors and urban shadowing, affects the production of energy. The Genetic Algorithm is employed to enhance the efficiency of BSC gains in Maroua’s solar environment. This optimization technique expedites the tracking process and minimizes oscillations in the GMPP. The adaptability of the learning algorithm to specific conditions improves energy generation, even in the challenging environment of Maroua. This study introduces a novel approach to enhance the efficiency of photovoltaic systems in Maroua, Cameroon, by tailoring them to the specific solar dynamics of the region. In terms of performance, our approach surpasses the INC-BSC, P&O-BSC, GA-BSC, and PSO-BSC methodologies. In practice, the stabilization period following shadowing typically requires fewer than three iterations. Additionally, our Maximum Power Point Tracking (MPPT) technology is based on the Global Maximum Power Point (GMPP) methodology, contrasting with alternative technologies that prioritize the Local Maximum Power Point (LMPP). This differentiation is particularly relevant in areas with partial shading, such as Maroua, where the use of LMPP-based technologies can result in power losses. The proposed method demonstrates significant performance by achieving a minimum 33% reduction in power losses. |
| format | Article |
| id | doaj-art-3330ce343c254513a19d08f9e1bb8412 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-3330ce343c254513a19d08f9e1bb84122025-01-26T12:34:57ZengNature PortfolioScientific Reports2045-23222024-02-0114112110.1038/s41598-024-53721-wRETRACTED ARTICLE: Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic Algorithm-optimized gainsSerge Raoul Dzonde Naoussi0Kenfack Tsobze Saatong1Reagan Jean Jacques Molu2Wulfran Fendzi Mbasso3Mohit Bajaj4Mohamed Louzazni5Milkias Berhanu6Salah Kamel7Technology and Applied Sciences Laboratory, U.I.T. of Douala, University of DoualaTechnology and Applied Sciences Laboratory, U.I.T. of Douala, University of DoualaTechnology and Applied Sciences Laboratory, U.I.T. of Douala, University of DoualaTechnology and Applied Sciences Laboratory, U.I.T. of Douala, University of DoualaDepartment of Electrical Engineering, Graphic Era (Deemed to be University)Science Engineer Laboratory for Energy LabSIPE, National School of Applied Sciences ENSAJ, Chouaib Doukkali UniversityDepartment of Electrical and Computer Engineering, Addis Ababa Science and Technology UniversityDepartment of Electrical Engineering, Faculty of Engineering, Aswan UniversityAbstract As the significance and complexity of solar panel performance, particularly at their maximum power point (MPP), continue to grow, there is a demand for improved monitoring systems. The presence of variable weather conditions in Maroua, including potential partial shadowing caused by cloud cover or urban buildings, poses challenges to the efficiency of solar systems. This study introduces a new approach to tracking the Global Maximum Power Point (GMPP) in photovoltaic systems within the context of solar research conducted in Cameroon. The system utilizes Genetic Algorithm (GA) and Backstepping Controller (BSC) methodologies. The Backstepping Controller (BSC) dynamically adjusts the duty cycle of the Single Ended Primary Inductor Converter (SEPIC) to align with the reference voltage of the Genetic Algorithm (GA) in Maroua’s dynamic environment. This environment, characterized by intermittent sunlight and the impact of local factors and urban shadowing, affects the production of energy. The Genetic Algorithm is employed to enhance the efficiency of BSC gains in Maroua’s solar environment. This optimization technique expedites the tracking process and minimizes oscillations in the GMPP. The adaptability of the learning algorithm to specific conditions improves energy generation, even in the challenging environment of Maroua. This study introduces a novel approach to enhance the efficiency of photovoltaic systems in Maroua, Cameroon, by tailoring them to the specific solar dynamics of the region. In terms of performance, our approach surpasses the INC-BSC, P&O-BSC, GA-BSC, and PSO-BSC methodologies. In practice, the stabilization period following shadowing typically requires fewer than three iterations. Additionally, our Maximum Power Point Tracking (MPPT) technology is based on the Global Maximum Power Point (GMPP) methodology, contrasting with alternative technologies that prioritize the Local Maximum Power Point (LMPP). This differentiation is particularly relevant in areas with partial shading, such as Maroua, where the use of LMPP-based technologies can result in power losses. The proposed method demonstrates significant performance by achieving a minimum 33% reduction in power losses.https://doi.org/10.1038/s41598-024-53721-wMaximum power point trackingBackstepping controllerGenetic algorithmPartial shading effectsPhotovoltaic array |
| spellingShingle | Serge Raoul Dzonde Naoussi Kenfack Tsobze Saatong Reagan Jean Jacques Molu Wulfran Fendzi Mbasso Mohit Bajaj Mohamed Louzazni Milkias Berhanu Salah Kamel RETRACTED ARTICLE: Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic Algorithm-optimized gains Scientific Reports Maximum power point tracking Backstepping controller Genetic algorithm Partial shading effects Photovoltaic array |
| title | RETRACTED ARTICLE: Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic Algorithm-optimized gains |
| title_full | RETRACTED ARTICLE: Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic Algorithm-optimized gains |
| title_fullStr | RETRACTED ARTICLE: Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic Algorithm-optimized gains |
| title_full_unstemmed | RETRACTED ARTICLE: Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic Algorithm-optimized gains |
| title_short | RETRACTED ARTICLE: Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic Algorithm-optimized gains |
| title_sort | retracted article enhancing mppt performance for partially shaded photovoltaic arrays through backstepping control with genetic algorithm optimized gains |
| topic | Maximum power point tracking Backstepping controller Genetic algorithm Partial shading effects Photovoltaic array |
| url | https://doi.org/10.1038/s41598-024-53721-w |
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