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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

Barbeiro, PNP (Autor) Outra; Teixeira, H (Autor) Outra; Jorge Pereira (Autor) FEP; Bessa, R (Autor) Outra

Ata de Conferência Internacional

Título: 2015 IEEE Eindhoven PowerTech, PowerTech 2015

IEEE Eindhoven PowerTech, PowerTech 2015

29 June 2015 through 2 July 2015

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 8 Citações

Classificação Científica

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia electrotécnica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

ID Authenticus: P-00K-2S6

DOI: 10.1109/ptc.2015.7232679

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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica