An innovative maximum power point tracking for photovoltaic systems operating under partially shaded conditions using Grey Wolf Optimization algorithm
Partial shading conditions (PSCs) may be unpredictable and difficult to forecast in large-scale solar photovoltaic (PV) systems. Potentially degrading the PV system's performance results from numerous peaks in the P–V curve caused by PSC. On the other hand, the PV system must be run at its maxi...
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Taylor & Francis Group
2024-10-01
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| Series: | Automatika |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2024.2388445 |
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| author | Muhannad J. Alshareef |
| author_facet | Muhannad J. Alshareef |
| author_sort | Muhannad J. Alshareef |
| collection | DOAJ |
| description | Partial shading conditions (PSCs) may be unpredictable and difficult to forecast in large-scale solar photovoltaic (PV) systems. Potentially degrading the PV system's performance results from numerous peaks in the P–V curve caused by PSC. On the other hand, the PV system must be run at its maximum power point (GMPP) to maximize its efficiency. Swarm optimization strategies have been employed to detect the GMPP; however, these methods are associated with an unacceptable amount of time to reach convergence. In this research, an innovative grey wolf optimization, abbreviated as NGWO, is presented as a solution to overcome the shortcomings of the standard GWO method, which includes long conversion times, a rate of failure, and large oscillations in a steady-state condition. This paper seeks to address these issues and fill a gap in research by enhancing the GWO's performance in tracking GMPP. The original GWO is modified to incorporate the Cuckoo Search (CS) abandoned process to shorten the time it takes for effective adoption. Based on the simulation finding, the proposed IGWO method beats other algorithms in most circumstances, particularly regarding tracking time and efficiency, where the average tracking time is 0.19s, and the average efficiency is 99.86%. |
| format | Article |
| id | doaj-art-a5ce1eadcbe14f71b6c66ea839ef4812 |
| institution | DOAJ |
| issn | 0005-1144 1848-3380 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj-art-a5ce1eadcbe14f71b6c66ea839ef48122025-08-20T02:48:53ZengTaylor & Francis GroupAutomatika0005-11441848-33802024-10-016541487150510.1080/00051144.2024.2388445An innovative maximum power point tracking for photovoltaic systems operating under partially shaded conditions using Grey Wolf Optimization algorithmMuhannad J. Alshareef0Department of Electrical Engineering, College of Engineering and Computing in Al-Qunfudhah, Umm Al-Qura University, Mecca, Saudi ArabiaPartial shading conditions (PSCs) may be unpredictable and difficult to forecast in large-scale solar photovoltaic (PV) systems. Potentially degrading the PV system's performance results from numerous peaks in the P–V curve caused by PSC. On the other hand, the PV system must be run at its maximum power point (GMPP) to maximize its efficiency. Swarm optimization strategies have been employed to detect the GMPP; however, these methods are associated with an unacceptable amount of time to reach convergence. In this research, an innovative grey wolf optimization, abbreviated as NGWO, is presented as a solution to overcome the shortcomings of the standard GWO method, which includes long conversion times, a rate of failure, and large oscillations in a steady-state condition. This paper seeks to address these issues and fill a gap in research by enhancing the GWO's performance in tracking GMPP. The original GWO is modified to incorporate the Cuckoo Search (CS) abandoned process to shorten the time it takes for effective adoption. Based on the simulation finding, the proposed IGWO method beats other algorithms in most circumstances, particularly regarding tracking time and efficiency, where the average tracking time is 0.19s, and the average efficiency is 99.86%.https://www.tandfonline.com/doi/10.1080/00051144.2024.2388445Grey wolf optimization (GWO)global maximum power point tracking (GMPPT)partial shading conditions (PSCs)photovoltaic (PV) system |
| spellingShingle | Muhannad J. Alshareef An innovative maximum power point tracking for photovoltaic systems operating under partially shaded conditions using Grey Wolf Optimization algorithm Automatika Grey wolf optimization (GWO) global maximum power point tracking (GMPPT) partial shading conditions (PSCs) photovoltaic (PV) system |
| title | An innovative maximum power point tracking for photovoltaic systems operating under partially shaded conditions using Grey Wolf Optimization algorithm |
| title_full | An innovative maximum power point tracking for photovoltaic systems operating under partially shaded conditions using Grey Wolf Optimization algorithm |
| title_fullStr | An innovative maximum power point tracking for photovoltaic systems operating under partially shaded conditions using Grey Wolf Optimization algorithm |
| title_full_unstemmed | An innovative maximum power point tracking for photovoltaic systems operating under partially shaded conditions using Grey Wolf Optimization algorithm |
| title_short | An innovative maximum power point tracking for photovoltaic systems operating under partially shaded conditions using Grey Wolf Optimization algorithm |
| title_sort | innovative maximum power point tracking for photovoltaic systems operating under partially shaded conditions using grey wolf optimization algorithm |
| topic | Grey wolf optimization (GWO) global maximum power point tracking (GMPPT) partial shading conditions (PSCs) photovoltaic (PV) system |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2024.2388445 |
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