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Operational optimal control of a water distribution network based on genetic algorithm

Asmargiannakis Petros

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URIhttp://purl.tuc.gr/dl/dias/07931947-359A-4582-AFEE-2985890CD9F9-
Identifierhttps://doi.org/10.26233/heallink.tuc.67596-
Languageen-
Extent128 pagesen
TitleΒέλτιστος έλεγχος για την διαχείριση δικτύου νερού, βασισμένος σε γενετικό αλγόριθμο el
TitleOperational optimal control of a water distribution network based on genetic algorithmen
CreatorAsmargiannakis Petrosen
CreatorΑσμαργιαννακης Πετροςel
Contributor [Thesis Supervisor]Stavrakakis Georgiosen
Contributor [Thesis Supervisor]Σταυρακακης Γεωργιοςel
Contributor [Committee Member]Zervakis Michalisen
Contributor [Committee Member]Ζερβακης Μιχαληςel
Contributor [Co-Supervisor]Sergaki Eleftheriaen
Contributor [Co-Supervisor]Σεργακη Ελευθεριαel
Contributor [Committee Member]Sergaki Eleftheriaen
Contributor [Committee Member]Σεργακη Ελευθεριαel
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
DescriptionΔιπλωματική Εργασία που υποβλήθηκε στη σχολή ΗΜΜΥ του Πολυτεχνείου Κρήτης για την πλήρωση προϋποθέσεων λήψης του Πτυχίου.el
Content SummaryDespite wide research, design of water distribution networks is not realized using optimization techniques. The main reason for this assumption is that the design of water distribution networks is evaluated mostly, as a least cost optimization problem. Another parameter for preferring the traditional modelling practice is that, existing optimization algorithms are not presented to the user as friendly as it should be. In this thesis we deal with the pump schedule optimization of water distribution systems. The aims and the possible applications of the presented method are significant and all of them are intended to solve a particular but realistic problem. A genetic controller for a midscale water pumping station is developed in order to optimize the operation plan of the system for minimum energy consumption. A genetic algorithm, GA optimizer, is used to decide the operation plan of the pumps taking into account power consumption. Upper and lower limits of the water level in the tank is constrained by the GA. The MATLAB Simscape blocks is used to build a full detailed model in a Simulink environment. The system consists of six pumps fed by a tank and deliver water to a buffer tank through the pipeline. This model is being processed many times to simulate, check and validate the behaviour of the system. Chania Vlite’s Pumping Station is taken as a case study. To optimize the operation plan of the system, the detailed model is used to design a preliminary control strategy which is used later to set the optimization problem. Fixed speed pump scheduling is found more efficient. The optimizer is tested and gave explainable results. Finally, the case study is tested within 24 hour time horizon and an accepted operation plan has resulted. Detailed plots and charts are generated using MATLAB to explain what is going on through all the levels of work in this thesis. All detailed information about the operation of this pump station is from OAK AE. en
Type of ItemΔιπλωματική Εργασίαel
Type of ItemDiploma Worken
Licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/en
Date of Item2017-03-17-
Date of Publication2017-
SubjectWater distribution systemsen
SubjectSpecific energy consumptionen
SubjectPump schedule optimizationen
Subject Genetic algorithmsen
Bibliographic CitationPetros Asmargiannakis, "Operational optimal control of a water distribution network based on genetic algorithm", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2017en
Bibliographic CitationΠέτρος Ασμαργιαννάκης, "Βέλτιστος έλεγχος για την διαχείριση δικτύου νερού, βασισμένος σε γενετικό αλγόριθμο ", Διπλωματική Εργασία, Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2017el

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