Please use this identifier to cite or link to this item: http://hdl.handle.net/10216/92426
Author(s): Barbeiro, PNP
Teixeira, H
Jorge Pereira
Bessa, R
Title: An ELM-AE State Estimator for real-time monitoring in poorly characterized distribution networks
Issue Date: 2015
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. © 2015 IEEE.
Subject: Engenharia electrotécnica, Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electrical engineering, Electronic engineering, Information engineering
Call Number: 114764
URI: http://hdl.handle.net/10216/92426
Source: 2015 IEEE Eindhoven PowerTech, PowerTech 2015
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: restrictedAccess
Appears in Collections:FEP - Artigo em Livro de Atas de Conferência Internacional

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