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Design of an electronic control system for maximizing the energy production of photovoltaic arrays, based on artificial intelligence techniques

Kalogerakis Christos

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URIhttp://purl.tuc.gr/dl/dias/E619DEF3-2055-4BA4-BE9A-DD97E466BA35-
Identifierhttps://doi.org/10.26233/heallink.tuc.83331-
Languageen-
Extent152 pagesen
TitleΣχεδίαση ηλεκτρονικού συστήματος ελέγχου για την μεγιστοποίηση της παραγωγής ενέργειας φωτοβολταϊκών συστοιχιών, βασισμένου σε τεχνικές τεχνητής νοημοσύνης el
TitleDesign of an electronic control system for maximizing the energy production of photovoltaic arrays, based on artificial intelligence techniquesen
CreatorKalogerakis Christosen
CreatorΚαλογερακης Χρηστοςel
Contributor [Thesis Supervisor]Koutroulis Eftychiosen
Contributor [Thesis Supervisor]Κουτρουλης Ευτυχιοςel
Contributor [Committee Member]Lagoudakis Michailen
Contributor [Committee Member]Λαγουδακης Μιχαηλel
Contributor [Committee Member]Kalaitzakis Konstantinosen
Contributor [Committee Member]Καλαϊτζακης Κωνσταντινοςel
PublisherΠολυτεχνείο Κρήτηςel
PublisherTechnical University of Creteen
Academic UnitTechnical University of Crete::School of Electrical and Computer Engineeringen
Academic UnitΠολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστώνel
Content Summary The subject of this thesis is the design of an electronic energy management system for maximizing the power generated by a photovoltaic (PV) array. For that purpose, an innovative Maximum Power Point Tracking (MPPT) algorithm was developed, which is based on reinforcement learning, in order to operate the PV array at the Maximum Power Point (MPP) under uniform and non-uniform incident solar irradiation conditions. The PV system under study consists of an MPPT control unit, a DC/DC Boost-type power converter and a battery. For the implementation of the MPPT control system, four different Q-learning-based MPPT methods and a Particle Swarm Optimization-based (PSO) MPPT method were implemented. The Qlearning-based MPPT algorithms were simulated for multiple alternative shading patterns of the PV array and their performance was compared to that of the PSO-based MPPT method. The simulation results demonstrated that the Q-learning-based methods exhibit faster convergence to the global MPP (GMPP) than the PSO-based MPPT method when an appropriate learning process has been applied before their execution. en
Type of ItemΔιπλωματική Εργασίαel
Type of ItemDiploma Worken
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2019-10-01-
Date of Publication2019-
SubjectMPPTen
SubjectPhotovoltaic systemen
SubjectPower electronicsen
SubjectQ-learningen
SubjectReinforcement learningen
Bibliographic CitationΧρήστος Καλογεράκης, "Σχεδίαση ηλεκτρονικού συστήματος ελέγχου για την μεγιστοποίηση της παραγωγής ενέργειας φωτοβολταϊκών συστοιχιών, βασισμένου σε τεχνικές τεχνητής νοημοσύνης ", Διπλωματική Εργασία, Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2019el
Bibliographic CitationChristos Kalogerakis, "Design of an electronic control system for maximizing the energy production of photovoltaic arrays, based on artificial intelligence techniques", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2019en

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