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An ELM-AE State Estimator for real-time monitoring in poorly characterized distribution networks

Title
An ELM-AE State Estimator for real-time monitoring in poorly characterized distribution networks
Type
Article in International Conference Proceedings Book
Year
2015
Authors
Barbeiro, PNP
(Author)
Other
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Teixeira, H
(Author)
Other
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Bessa, R
(Author)
Other
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Conference proceedings International
IEEE Eindhoven PowerTech, PowerTech 2015
29 June 2015 through 2 July 2015
Scientific classification
CORDIS: Technological sciences > Engineering > Electrical engineering
FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering
Other information
Authenticus ID: P-00K-2S6
Abstract (EN): 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.
Language: English
Type (Professor's evaluation): Scientific
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