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

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...

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Bibliographic Details
Main Authors: Pereira Barbeiro, P. N., Teixeira, H., Pereira, Jorge, Bessa, R.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
autoencoders
distribution state estimation
extreme learning machine
Monitoring
Real-time systems
smart distribution networks
State estimation
Training
Voltage measurement
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Summary: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.
DOI:10.1109/PTC.2015.7232679