<p>U ovoj doktorskoj disertaciji je predstavljena je nova metoda za<br />detekciju propada napona, zasnovana na Rekurentnoj<br />neuronskoj mreži i analizi u harmonijskom domenu. Metoda je<br />namenjena za primenu u savremenim distributivnim mrežama<br />koje sadrže obnovljive izvore, i u skladu sa tim je optimizovana i<br />testirana. Pametna metoda postiže izuzetne rezultate u brzini<br />detekcije, sa prosečnim vremenom detekcije manjim od 1 ms, uz<br />izuzetnu pouzdanost (preko 97%). U doktorskoj disertaciji<br />dokazana je i druga hipoteza, a to je da je moguće predvideti<br />dubinu propada algoritmom zasnovanim na harmonijskoj analizi.</p> / <p>In this PhD thesis, a novel method for the detection of voltage dips<br />(sags), based on the Recurrent Neural Network and analysis in the<br />frequency domain, is presented. The method is intended for use in<br />the modern distribution grids that contains renewable sources, and<br />accordingly it is optimized and tested. The smart method achieves<br />exceptional results in detection speed, with an average detection<br />time of less than 1 ms and with high reliability (over 97%). In the<br />PhD thesis, another hypothesis is proved, which claims that is<br />possible to predict the depth of dip with algorithm based on the<br />harmonic analysis.</p>
| Identifer | oai:union.ndltd.org:uns.ac.rs/oai:CRISUNS:(BISIS)110015 |
| Date | 29 March 2019 |
| Creators | Stanisavljević Aleksandar |
| Contributors | Katić Vladimir, Strezoski Vladimir, Dumnić Boris, Grabić Stevan, Mujović Saša |
| Publisher | Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, University of Novi Sad, Faculty of Technical Sciences at Novi Sad |
| Source Sets | University of Novi Sad |
| Language | Serbian |
| Detected Language | Unknown |
| Type | PhD thesis |
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