Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/93951
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dc.creatorP. N. Pereira Barbeiro
dc.creatorH. Teixeira
dc.creatorJ. Krstulovic
dc.creatorJ. Pereira
dc.creatorF. J. Soares
dc.date.accessioned2019-03-26T00:09:13Z-
dc.date.available2019-03-26T00:09:13Z-
dc.date.issued2015
dc.identifier.issn0378-7796
dc.identifier.othersigarra:99712
dc.identifier.urihttps://hdl.handle.net/10216/93951-
dc.description.abstractThe three-phase state estimation algorithms developed for distribution systems (DS) are based on traditional approaches, requiring components modeling and the complete knowledge of grid parameters. These algorithms are capable of dealing with the particular characteristics of DS but cannot be used in cases where grid topology and parameters are unknown, which is the most common situation in existing low voltage grids. This paper presents a novel three-phase state estimator for DS that enables the explicit estimation of voltage magnitudes and phase angles in all phases, neutral, and ground wires even when grid topology and parameters are unknown. The proposed approach is based on the use of auto-associative neural networks, the autoencoders (AE), which only require an historical database and few quasi-real-time measurements to perform an effective state estimation. Two test cases were used to evaluate the algorithm's performance: a low and a medium voltage grid. Results show that the algorithm provides accurate results even without information about grid topology and parameters. Several tests were performed to evaluate the best AE configuration. It was found that training an AE for each network feeder leads generally to better results than having a single AE for the entire system. The same happened when different AE were trained for each network phase in comparison with a single AE for the three phases.
dc.language.isoeng
dc.rightsrestrictedAccess
dc.subjectEngenharia electrotécnica, electrónica e informática
dc.subjectElectrical engineering, Electronic engineering, Information engineering
dc.titleExploiting autoencoders for three-phase state estimation in unbalanced distributions grids
dc.typeArtigo em Revista Científica Internacional
dc.contributor.uportoFaculdade de Economia
dc.contributor.uportoFaculdade de Engenharia
dc.identifier.doi10.1016/j.epsr.2015.02.003
dc.identifier.authenticusP-00A-7ZX
dc.subject.fosCiências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
dc.subject.fosEngineering and technology::Electrical engineering, Electronic engineering, Information engineering
Appears in Collections:FEP - Artigo em Revista Científica Internacional
FEUP - Artigo em Revista Científica Internacional

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