Apr 29, 2022 · We propose a deep active learning framework for solvability prediction in power systems. Compared with passive learning where the training is performed after ...
Jul 27, 2020 · Abstract:Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism.
[PDF] Deep Active Learning for Solvability Prediction in Power Systems
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In this let- ter, we propose a deep active learning framework for solvability prediction in power systems.
To this end, we propose a deep learning-based approach to predict the solvability. Our method consists of two phases: off-line training and online prediction. ...
This paper innovatively uses deep active learning for solvability prediction. II. DEEP ACTIVE LEARNING FOR POWER FLOW SOLVABILITY. APPROXIMATION. Consider an NB ...
In the active learning framework, the acquisition functions, which correspond to different sampling strategies, are defined in terms of the on-the-fly posterior ...
Aug 6, 2020 · PDF | Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism.
This letter proposes the deep active learning framework for solvability prediction in power systems with full AC power flow models. In this problem, sampling ...
In this letter, we propose a deep active learning framework for solvability prediction in power systems. Compared with passive learning where the training is ...
Hong, R. Yao, “Deep active learning for solvability prediction in power systems,”J. Mod. Power Syst. Clean Energy, vol. 10, no. 6, pp. 1773–1777, 2022 ...