Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/1CA9A46C-5FE8-486F-BCD6-469104FE4C80 | - |
| Αναγνωριστικό | https://doi.org/10.3390/app10020700 | - |
| Αναγνωριστικό | https://www.mdpi.com/2076-3417/10/2/700/htm | - |
| Γλώσσα | en | - |
| Μέγεθος | 19 pages | en |
| Μέγεθος | 7,28 megabytes | en |
| Τίτλος | Global MPPT based on machine-learning for PV arrays operating under partial shading conditions | en |
| Δημιουργός | Kalogerakis Christos | en |
| Δημιουργός | Καλογερακης Χρηστος | el |
| Δημιουργός | Koutroulis Eftychios | en |
| Δημιουργός | Κουτρουλης Ευτυχιος | el |
| Δημιουργός | Lagoudakis Michail | en |
| Δημιουργός | Λαγουδακης Μιχαηλ | el |
| Εκδότης | MDPI | en |
| Περιγραφή | This article belongs to the special issue Advancing grid-connected renewable generation systems 2019 | en |
| Περίληψη | A global maximum power point tracking (GMPPT) process must be applied for detecting the position of the GMPP operating point in the minimum possible search time in order to maximize the energy production of a photovoltaic (PV) system when its PV array operates under partial shading conditions. This paper presents a novel GMPPT method which is based on the application of a machine-learning algorithm. Compared to the existing GMPPT techniques, the proposed method has the advantage that it does not require knowledge of the operational characteristics of the PV modules comprising the PV system, or the PV array structure. Additionally, due to its inherent learning capability, it is capable of detecting the GMPP in significantly fewer search steps and, therefore, it is suitable for employment in PV applications, where the shading pattern may change quickly (e.g., wearable PV systems, building-integrated PV systems etc.). The numerical results presented in the paper demonstrate that the time required for detecting the global MPP, when unknown partial shading patterns are applied, is reduced by 80.5%–98.3% by executing the proposed Q-learning-based GMPPT algorithm, compared to the convergence time required by a GMPPT process based on the particle swarm optimization (PSO) algorithm. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2021-09-27 | - |
| Ημερομηνία Δημοσίευσης | 2020 | - |
| Θεματική Κατηγορία | Machine learning | en |
| Θεματική Κατηγορία | Maximum power point tracking (MPPT) | en |
| Θεματική Κατηγορία | Particle swarm optimization (PSO) | en |
| Θεματική Κατηγορία | Photovoltaic systems | en |
| Θεματική Κατηγορία | Reinforcement learning | en |
| Θεματική Κατηγορία | Q-learning | en |
| Βιβλιογραφική Αναφορά | C. Kalogerakis, E. Koutroulis, and M. G. Lagoudakis, “Global MPPT based on machine-learning for PV arrays operating under partial shading conditions,” Appl. Sci., vol. 10, no. 2, Jan. 2020. doi: 10.3390/app10020700 | en |
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