ROBUST SLIDING-MODE-BASED LEARNING FOR MAXIMUM POWER POINT TRACKING PHOTOVOLTAICS SYSTEM
Maximum power point tracking (MPPT) is crucial for optimizing the energy extraction from solar modules in photovoltaic (PV) systems. This paper focuses on maximizing the energy extraction from solar panels and explores the important aspects of MPPT technology in PV systems. The sliding mode learning...
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| Format: | Article |
| Language: | English |
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Faculty of Engineering, University of Kufa
2025-04-01
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| Series: | Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ |
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| Online Access: | https://journal.uokufa.edu.iq/index.php/kje/article/view/15628 |
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| author | Abdal-Razak Shehab Hadi Adnan Alamili Ali Abdyasser Kadhum |
| author_facet | Abdal-Razak Shehab Hadi Adnan Alamili Ali Abdyasser Kadhum |
| author_sort | Abdal-Razak Shehab Hadi |
| collection | DOAJ |
| description | Maximum power point tracking (MPPT) is crucial for optimizing the energy extraction from solar modules in photovoltaic (PV) systems. This paper focuses on maximizing the energy extraction from solar panels and explores the important aspects of MPPT technology in PV systems. The sliding mode learning controller (SMLC) created for MPPT provides a new way to deal with environmental factors, including radiation and temperature fluctuations. The study compares the disturbance-insensitive SMLC with linear proportional-integral-derivative (PID) controllers and conventional sliding mode controllers (CSMC). The SMLC enhances the maximum energy extraction by tracking the reference voltage signal using the perturb and observe (P&O) algorithm. Moreover, the controller can adapt to the dynamic changes in PV characteristics thanks to the learning system. The result shows that the proposed SMLC offers significant benefits, especially in challenging operating conditions. It demonstrates superior vibration-free performance, fast response (with a settling time of 24.7 ms), and smooth and precise tracking compared to other controllers. |
| format | Article |
| id | doaj-art-c0366678e07e4d1e8f02ccde1d365beb |
| institution | DOAJ |
| issn | 2071-5528 2523-0018 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Faculty of Engineering, University of Kufa |
| record_format | Article |
| series | Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ |
| spelling | doaj-art-c0366678e07e4d1e8f02ccde1d365beb2025-08-20T03:11:54ZengFaculty of Engineering, University of KufaMağallaẗ Al-kūfaẗ Al-handasiyyaẗ2071-55282523-00182025-04-01160224926210.30572/2018/KJE/160215ROBUST SLIDING-MODE-BASED LEARNING FOR MAXIMUM POWER POINT TRACKING PHOTOVOLTAICS SYSTEMAbdal-Razak Shehab Hadi0https://orcid.org/0000-0002-5329-5880Adnan Alamili1https://orcid.org/0000-0002-8316-6553Ali Abdyasser Kadhum2Electrical Engineering Department, Faculty of Engineering, University of Kufa, Kufa, IraqElectrical Engineering Department, Faculty of Engineering, University of Kufa, Kufa, IraqElectric Technical Department, Kufa Technical Institute, Al-Furat Al-Awsat Technical University, IraqMaximum power point tracking (MPPT) is crucial for optimizing the energy extraction from solar modules in photovoltaic (PV) systems. This paper focuses on maximizing the energy extraction from solar panels and explores the important aspects of MPPT technology in PV systems. The sliding mode learning controller (SMLC) created for MPPT provides a new way to deal with environmental factors, including radiation and temperature fluctuations. The study compares the disturbance-insensitive SMLC with linear proportional-integral-derivative (PID) controllers and conventional sliding mode controllers (CSMC). The SMLC enhances the maximum energy extraction by tracking the reference voltage signal using the perturb and observe (P&O) algorithm. Moreover, the controller can adapt to the dynamic changes in PV characteristics thanks to the learning system. The result shows that the proposed SMLC offers significant benefits, especially in challenging operating conditions. It demonstrates superior vibration-free performance, fast response (with a settling time of 24.7 ms), and smooth and precise tracking compared to other controllers.https://journal.uokufa.edu.iq/index.php/kje/article/view/15628photovoltaic systemmaximum power point tracking (mppt)perturb and observe (p&o) algorithmpid controllersmlc |
| spellingShingle | Abdal-Razak Shehab Hadi Adnan Alamili Ali Abdyasser Kadhum ROBUST SLIDING-MODE-BASED LEARNING FOR MAXIMUM POWER POINT TRACKING PHOTOVOLTAICS SYSTEM Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ photovoltaic system maximum power point tracking (mppt) perturb and observe (p&o) algorithm pid controller smlc |
| title | ROBUST SLIDING-MODE-BASED LEARNING FOR MAXIMUM POWER POINT TRACKING PHOTOVOLTAICS SYSTEM |
| title_full | ROBUST SLIDING-MODE-BASED LEARNING FOR MAXIMUM POWER POINT TRACKING PHOTOVOLTAICS SYSTEM |
| title_fullStr | ROBUST SLIDING-MODE-BASED LEARNING FOR MAXIMUM POWER POINT TRACKING PHOTOVOLTAICS SYSTEM |
| title_full_unstemmed | ROBUST SLIDING-MODE-BASED LEARNING FOR MAXIMUM POWER POINT TRACKING PHOTOVOLTAICS SYSTEM |
| title_short | ROBUST SLIDING-MODE-BASED LEARNING FOR MAXIMUM POWER POINT TRACKING PHOTOVOLTAICS SYSTEM |
| title_sort | robust sliding mode based learning for maximum power point tracking photovoltaics system |
| topic | photovoltaic system maximum power point tracking (mppt) perturb and observe (p&o) algorithm pid controller smlc |
| url | https://journal.uokufa.edu.iq/index.php/kje/article/view/15628 |
| work_keys_str_mv | AT abdalrazakshehabhadi robustslidingmodebasedlearningformaximumpowerpointtrackingphotovoltaicssystem AT adnanalamili robustslidingmodebasedlearningformaximumpowerpointtrackingphotovoltaicssystem AT aliabdyasserkadhum robustslidingmodebasedlearningformaximumpowerpointtrackingphotovoltaicssystem |