<p>Doktorskom disertacijom je dokazano da postojeće metode detekcije i identifikacije loših podataka nisu primenjive na distributivne mreže usled njihovih specifičnosti u stepenu redundanse merenja i broja pseudo merenja. Dodatno, razvijeni su algoritmi detekcije loših oblasti primenom dekuplovanog Hi-kvadrat testa, identifikacije loših merenja primenom novo definisanih izbeljenih reziduala, estimacije fazne konektivnosti primenom uslovnih ograničenja u estimatoru stanja, i korekcije pseudo merenja primenom informacija sa pametnih brojila. Navedeni algoritmi su specijalizovani za distributivne mreže i verifikovani primenom na dva test sistema.</p> / <p>The doctoral dissertation has demonstrated that conventional bad data detection and<br />identification methods cannot be efficiently applied in distribution networks, due to<br />their characteristics such as low measurement redundancy, number of pseudo<br />measurements and level of measurements correlation. In addition, the doctoral<br />dissertation described newly developed algorithms for bad area detection based on<br />decoupled Chi-squares test, bad data identification using newly defined whitened<br />residuals, estimation of phase connectivity by extension of state estimation with<br />conditional constraints and correction of pseudo measurements using AMI data. The<br />mentioned algorithms are specialized for distribution networks and verified through<br />simulation on two test systems.</p>
| Identifer | oai:union.ndltd.org:uns.ac.rs/oai:CRISUNS:(BISIS)104436 |
| Date | 30 June 2017 |
| Creators | Krsman Vladan |
| Contributors | Sarić Andrija, Strezoski Vladimir, Švenda Goran, Popović Dragan, Ranković Aleksandar |
| 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 | English |
| Type | PhD thesis |
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