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Distribuirana estimacija stanja u elektroenergetskimn sistemima upotrebom probabilističkih grafičkih modela

dc.contributor.advisorVukobratović, Dejan
dc.contributor.otherSarić, Andrija
dc.contributor.otherPopovski, Petar
dc.contributor.otherStefanović, Čedomir
dc.contributor.otherDžafić, Izudin
dc.contributor.otherJakovetić, Dušan
dc.contributor.otherVukobratović, Dejan
dc.creatorĆosović, Mirsad
dc.date.accessioned2019-07-12T15:43:15Z
dc.date.available2019-07-12T15:43:15Z
dc.date.available2020-07-03T14:06:52Z
dc.date.issued2019-05-29
dc.identifier.urihttps://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija156172037958568.pdf?controlNumber=(BISIS)108459&fileName=156172037958568.pdf&id=13046&source=NaRDuS&language=srsr
dc.identifier.urihttps://nardus.mpn.gov.rs/handle/123456789/11222
dc.identifier.urihttps://www.cris.uns.ac.rs/record.jsf?recordId=108459&source=NaRDuS&language=srsr
dc.identifier.urihttps://www.cris.uns.ac.rs/DownloadFileServlet/IzvestajKomisije156234243261625.pdf?controlNumber=(BISIS)108459&fileName=156234243261625.pdf&id=13090&source=NaRDuS&language=srsr
dc.identifier.urinull/DownloadFileServlet/IzvestajKomisije156234243261625.pdf?controlNumber=(BISIS)108459&fileName=156234243261625.pdf&id=13090
dc.description.abstractWe present a detailed study on application of factor graphs and the belief propagation (BP) algorithm to the power system state estimation (SE) problem. We start from the BP solution for the linear DC model, for which we provide a detailed convergence analysis. Using BPbased DC model we propose a fast real-time state estimator for the power system SE. The proposed estimator is easy to distribute and parallelize, thus alleviating computational limitations and allowing for processing measurements in real time. The presented algorithm may run as a continuous process, with each new measurement being seamlessly processed by the distributed state estimator. In contrast to the matrixbased SE methods, the BP approach is robust to illconditioned scenarios caused by significant differences between measurement variances, thus resulting in a solution that eliminates observability analysis. Using the DC model, we numerically demonstrate the performance of the state estimator in a realistic real-time system model with asynchronous measurements. We note that the extension to the non-linear SE is possible within the same framework. Using insights from the DC model, we use two different approaches to derive the BP algorithm for the non-linear model. The first method directly applies BP methodology, however, providing only approximate BP solution for the non-linear model. In the second approach, we make a key further step by providing the solution in which the BP is applied sequentially over the non-linear model, akin to what is done by the Gauss-Newton method. The resulting iterative Gauss-Newton belief propagation (GN-BP) algorithm can be interpreted as a distributed Gauss- Newton method with the same accuracy as the centralized SE, however, introducing a number of advantages of the BP framework. The thesis provides extensive numerical study of the GN-BP algorithm, provides details on its convergence behavior, and gives a number of useful insights for its implementation. Finally, we define the bad data test based on the BP algorithm for the non-linear model. The presented model establishes local criteria to detect and identify bad data measurements. We numerically demonstrate that the BP-based bad data test significantly improves the bad data detection over the largest normalized residual test.en
dc.description.abstractGlavni rezultati ove teze su dizajn i analiza novih algoritama za rešavanje problema estimacije stanja baziranih na faktor grafovima i „Belief Propagation“ (BP) algoritmu koji se mogu primeniti kao centralizovani ili distribuirani estimatori stanja u elektroenergetskim sistemima. Na samom početku, definisan je postupak za rešavanje linearnog (DC) problema korišćenjem BP algoritma. Pored samog algoritma data je analiza konvergencije i predloženo je rešenje za unapređenje konvergencije. Algoritam se može jednostavno distribuirati i paralelizovati, te je pogodan za estimaciju stanja u realnom vremenu, pri čemu se informacije mogu prikupljati na asinhroni način, zaobilazeći neke od postojećih rutina, kao npr. provera observabilnosti sistema. Proširenje algoritma za nelinearnu estimaciju stanja je moguće unutar datog modela. Dalje se predlaže algoritam baziran na probabilističkim grafičkim modelima koji je direktno primenjen na nelinearni problem estimacije stanja, što predstavlja logičan korak u tranziciji od linearnog ka nelinearnom modelu. Zbog nelinearnosti funkcija, izrazi za određenu klasu poruka ne mogu se dobiti u zatvorenoj formi, zbog čega rezultujući algoritam predstavlja aproksimativno rešenje. Nakon toga se predlaže distribuirani Gaus- Njutnov metod baziran na probabilističkim grafičkim modelima i BP algoritmu koji postiže istu tačnost kao i centralizovana verzija Gaus-Njutnovog metoda za estimaciju stanja, te je dat i novi algoritam za otkrivanje nepouzdanih merenja (outliers) prilikom merenja električnih veličina. Predstavljeni algoritam uspostavlja lokalni kriterijum za otkrivanje i identifikaciju nepouzdanih merenja, a numerički je pokazano da algoritam značajno poboljšava detekciju u odnosu na standardne metode.sr
dc.languageen
dc.publisherУниверзитет у Новом Саду, Факултет техничких наукаsr
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/607774/EU//
dc.rightsopenAccessen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceУниверзитет у Новом Садуsr
dc.subjectState Estimationen
dc.subjectEstimacija stanjasr
dc.subjectElectric Power Systemen
dc.subjectFactor Graphsen
dc.subjectBelief Propagationen
dc.subjectDistributed Gauss-Newton Methoden
dc.subjectElektroenergetski sistemisr
dc.subjectFaktorgrafovisr
dc.subjectBelief Propagationsr
dc.subjectDistribuirani Gaus-Njutnovmetodsr
dc.titleDistributed State Estimation in Power Systems using Probabilistic Graphical Modelsen
dc.title.alternativeDistribuirana estimacija stanja u elektroenergetskimn sistemima upotrebom probabilističkih grafičkih modelasr
dc.typedoctoralThesisen
dc.rights.licenseBY-NC-ND
dc.identifier.fulltexthttps://nardus.mpn.gov.rs/bitstream/id/41541/Disertacija.pdf
dc.identifier.fulltexthttp://nardus.mpn.gov.rs/bitstream/id/41542/IzvestajKomisije.pdf
dc.identifier.fulltexthttps://nardus.mpn.gov.rs/bitstream/id/41542/IzvestajKomisije.pdf
dc.identifier.fulltexthttp://nardus.mpn.gov.rs/bitstream/id/41541/Disertacija.pdf
dc.identifier.rcubhttps://hdl.handle.net/21.15107/rcub_nardus_11222


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