Reconstructing missing data in State Estimation with autoencoders

dc.contributor.author Cristiano Moreira en
dc.contributor.author Jakov Opara en
dc.contributor.author Hrvoje Keko en
dc.contributor.author Jorge Correia Pereira en
dc.contributor.author Vladimiro Miranda en
dc.date.accessioned 2017-11-16T13:31:46Z
dc.date.available 2017-11-16T13:31:46Z
dc.date.issued 2012 en
dc.description.abstract This paper presents the proof of concept for a new solution to the problem of recomposing missing information at the SCADA of EMS/DMS (Energy/Distribution Management Systems), through the use of off-line trained autoencoders. These are neural networks with a special architecture, which allows them to store knowledge about a system in a non-linear manifold characterized by their weights. Suitable algorithms may then recompose missing inputs (measurements). The paper shows that, trained with adequate information, autoencoders perform well in recomposing missing voltage and power values, and focuses on the particularly important application of inferring the topology of the network when information about switch status is absent. Examples with the IEEE RTS 24 bus network are presented to illustrate the concept and technique. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/2339
dc.language eng en
dc.relation 208 en
dc.relation 4811 en
dc.relation 5409 en
dc.relation 1809 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Reconstructing missing data in State Estimation with autoencoders en
dc.type article en
dc.type Publication en
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