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doctoral thesis
Detekcija otočnog pogona foto naponskih sustava metodama umjetne inteligencije
Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2019. urn:nbn:hr:200:505475
Mlakić, Dragan
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Power Engineering
Chair of Power Systems and Substations

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Mlakić, D. (2019). Detekcija otočnog pogona foto naponskih sustava metodama umjetne inteligencije (Doctoral thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:505475

Mlakić, Dragan. "Detekcija otočnog pogona foto naponskih sustava metodama umjetne inteligencije." Doctoral thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2019. https://urn.nsk.hr/urn:nbn:hr:200:505475

Mlakić, Dragan. "Detekcija otočnog pogona foto naponskih sustava metodama umjetne inteligencije." Doctoral thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2019. https://urn.nsk.hr/urn:nbn:hr:200:505475

Mlakić, D. (2019). 'Detekcija otočnog pogona foto naponskih sustava metodama umjetne inteligencije', Doctoral thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 14 March 2025, https://urn.nsk.hr/urn:nbn:hr:200:505475

Mlakić D. Detekcija otočnog pogona foto naponskih sustava metodama umjetne inteligencije [Doctoral thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2019 [cited 2025 March 14] Available at: https://urn.nsk.hr/urn:nbn:hr:200:505475

D. Mlakić, "Detekcija otočnog pogona foto naponskih sustava metodama umjetne inteligencije", Doctoral thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:200:505475

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Metadata
TitleDetekcija otočnog pogona foto naponskih sustava metodama umjetne inteligencije
Title (english)Detection of island powered photo voltage systems by artificial intelligence methods
AuthorDragan Mlakić
MentorSrete Nikolovski (mentor)
Committee memberMarinko Barukčić (predsjednik povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Power Engineering)
(Chair of Power Systems and Substations)
Osijek
Defense date and country2019-05-09, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Power Engineering
Universal decimal classification
(UDC)		621.3 - Electrical engineering
Abstract
U doktorskom radu je predstavljena metodologija detekcije otočnog rada distribuiranih izvora energije uz pomoć metoda umjetne inteligencije (umjetne inteligencije) sa ciljem zadržavanja pasivne prirode metode detekcije a povećanja preciznosti, smanjenja vremena detekcije i zone ne-detekcije (zone ne detekcije). Predložena metodologija se sastoji od tri cjeline realizirane kroz 9 poglavlja: snimanje parametara mreže sa pametnim brojilom, treniranje umjetne inteligencije metoda sa slogovima... More podataka, testiranje metoda kroz standarde IEEE 1547 i UL 1741. Detekcija otočnog rada je realizirano kroz tri predložene metode: Adaptivni sustav neuro-neizrazitog zaključivanja(eng. Adaptive Neuro-Fuzzy Inference System - ANFIS), Duboko učenje (eng. Deep Learning - DL) i Metoda potpornih vektora (eng. Support Vector Machine - MPV). Mjerenja su prikupljeni podaci sa realnog foto-naponskog (FN) sustava uz pomoć kojih je modeliran model u programskom alatu Matlabu/Simulink za testiranje navedenih umjetne inteligencije metoda. Također su izmjereni rezultati korišteni za treniranje kognitivnih metoda. Ocjenjivanje efikasnosti metoda se radi prema navedenim standardima, te još tri vrste kvara na niskonaponskoj distribucijskoj mreži, još jednim distribuiranim izvorom na istoj točki spajanja distribuiranog izvora sa distribucijskoj mreži-om, brzo punjenje električnih i hibridnih automobila (PHEV), te promjeni faktora kvalitete mreže (Qf). Na kraju su predstavljeni rezultati od sve tri predložene umjetne inteligencije metode, kao sumarni prikaz u izvješću u poglavlju 8, te diskutirana moguća daljnja istraživanja u cilju poboljšanja navedena tri faktora metoda za detekciju otočnog rada. Less
Abstract (english)
The methodology for detecting islanding of Distributed Generators (DGs) in Distribution Network (DN) using the method of artificial intelligence (AI) is presented in the dissertation with the aim of retaining the passive nature of the detection method and increasing the precision, reduction of detection time and non-detection zone (NDZ). The proposed methodology consists of three units realized through 9 chapters: smart metering network parameters, training of AI methods using recorded... More data, testing methods through standards IEEE 1547 and UL 1741. Detection of the islanding is done through three proposed methods: Adaptive Neuro-Fuzzy Inference System 239 (ANFIS), Deep Learning (DL), and Vector Support Machine (MPV). The measurements were collected from the real photographic (FN) system used in modeling of testing simulation environment in Matlab / Simulink software to test these Artificial Intelligence methods. Measurement results were also used to train cognitive methods. Efficiency evaluation of the method is done according to the mentioned standards and three other situations in microgrid: fault on the low voltage distribution network, another distribution sources at the same point of common coupling of distribution source with distribution network, rapid charging of electric and hybrid cars (PHEV) and change of network quality factors (Qf). Finally, the results of all three proposed Artificial Intelligence methods are presented as a summary report in the chapter 8 and discussed possible further research to improve mentioned three factors of inslanding detection methods. Less
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:505475
Promotion2019-06-14
Study programmeTitle: Postgraduate doctoral study programme; specializations in: branch Power Engineering and Communications, branch Informatics
Course: branch Power Engineering and Communications
Study programme type: university
Study level: postgraduate
Academic / professional title: doktor/doktorica znanosti, područje tehničkih znanosti, polje elektrotehnika (doktor/doktorica znanosti, područje tehničkih znanosti, polje elektrotehnika)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
RepositoryFaculty of Electrical Engineering, Computer Science and Information Technology Osijek
Created on2019-09-03 12:08:13
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