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Global MPPT based on machine-learning for PV arrays operating under partial shading conditions

Kalogerakis Christos, Koutroulis Eftychios, Lagoudakis Michail

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/1CA9A46C-5FE8-486F-BCD6-469104FE4C80-
Αναγνωριστικόhttps://doi.org/10.3390/app10020700-
Αναγνωριστικόhttps://www.mdpi.com/2076-3417/10/2/700/htm-
Γλώσσαen-
Μέγεθος19 pagesen
Μέγεθος7,28 megabytesen
ΤίτλοςGlobal MPPT based on machine-learning for PV arrays operating under partial shading conditionsen
ΔημιουργόςKalogerakis Christosen
ΔημιουργόςΚαλογερακης Χρηστοςel
ΔημιουργόςKoutroulis Eftychiosen
ΔημιουργόςΚουτρουλης Ευτυχιοςel
ΔημιουργόςLagoudakis Michailen
ΔημιουργόςΛαγουδακης Μιχαηλel
ΕκδότηςMDPIen
ΠεριγραφήThis article belongs to the special issue Advancing grid-connected renewable generation systems 2019en
ΠερίληψηA 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
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2021-09-27-
Ημερομηνία Δημοσίευσης2020-
Θεματική ΚατηγορίαMachine learningen
Θεματική ΚατηγορίαMaximum power point tracking (MPPT)en
Θεματική ΚατηγορίαParticle swarm optimization (PSO)en
Θεματική ΚατηγορίαPhotovoltaic systemsen
Θεματική ΚατηγορίαReinforcement learningen
Θεματική ΚατηγορίαQ-learningen
Βιβλιογραφική ΑναφοράC. 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/app10020700en

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