An ELM-AE State Estimator for Real-Time Monitoring in Poorly Characterized Distribution Networks

dc.contributor.author Pedro Pereira Barbeiro en
dc.contributor.author Henrique Silva Teixeira en
dc.contributor.author Jorge Correia Pereira en
dc.contributor.author Ricardo Jorge Bessa en
dc.date.accessioned 2018-01-05T19:13:21Z
dc.date.available 2018-01-05T19:13:21Z
dc.date.issued 2015 en
dc.description.abstract In this paper a Distribution State Estimator (DSE) tool suitable for real-time monitoring in poorly characterized low voltage networks is presented. An Autoencoder (AE) properly trained with Extreme Learning Machine (ELM) technique is the "brain" of the DSE. The estimation of system state variables, i.e., voltage magnitudes and phase angles is performed with an Evolutionary Particle Swarm Optimization (EPSO) algorithm that makes use of the already trained AE. By taking advantage of historical data and a very limited number of quasi real-time measurements, the presented approach turns possible monitoring networks where information of topology and parameters is not available. Results show improvements in terms of estimation accuracy and time performance when compared to other similar DSE tools that make use of the traditional back-propagation based algorithms for training execution. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/5589
dc.identifier.uri http://dx.doi.org/10.1109/ptc.2015.7232679 en
dc.language eng en
dc.relation 5284 en
dc.relation 5217 en
dc.relation 1809 en
dc.relation 4882 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title An ELM-AE State Estimator for Real-Time Monitoring in Poorly Characterized Distribution Networks en
dc.type conferenceObject en
dc.type Publication en
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