Application of Deep Neural Networks to Distribution System State Estimation and Forecasting

Application of Deep Neural Networks to Distribution System State Estimation and Forecasting

Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and win...

Ausführliche Beschreibung

Bibliographische Detailangaben
Hauptverfasser: James P. Carmichael, Yuan Liao
Format: Artikel
Sprache:English
Veröffentlicht: Frontiers Media S.A. 2022-01-01
Schriftenreihe:Frontiers in Sustainable Cities
Schlagworte:
artificial neural networks (ANNs)
multilayer perceptron networks (MLPs)
convolutional neural networks (CNNS)
long short-term memory networks (LSTMs)
state estimation
state forecasting
Online Zugang:https://www.frontiersin.org/articles/10.3389/frsc.2021.814037/full

Online

https://www.frontiersin.org/articles/10.3389/frsc.2021.814037/full

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