A stacked denoising autoencoder learns the mapping from operating conditions to OPF solutions so that Monte-Carlo samples for probabilistic OPF can be evaluated without repeated nonlinear optimization.
Available: https://figshare.com/articles/Case_1_xlsx/7291769
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SP 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Fast Calculation of Probabilistic Optimal Power Flow: A Deep Learning Approach
A stacked denoising autoencoder learns the mapping from operating conditions to OPF solutions so that Monte-Carlo samples for probabilistic OPF can be evaluated without repeated nonlinear optimization.