Please use this identifier to cite or link to this item:
Title: An ELM-AE State Estimator for Real-Time Monitoring in Poorly Characterized Distribution Networks
Authors: Pedro Pereira Barbeiro
Henrique Silva Teixeira
Jorge Correia Pereira
Ricardo Jorge Bessa
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.
metadata.dc.type: conferenceObject
Appears in Collections:CPES - Articles in International Conferences

Files in This Item:
File Description SizeFormat 
P-00K-2S6.pdf721.68 kBAdobe PDFThumbnail

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.