2 citations · 2 across the 2 of their papers we have counts for
4 papers · 1 filter
Continual Adversarial Reinforcement Learning (CARL) of False Data Injection detection: forgetting and explainability
Pooja Aslami, Kejun Chen, Timothy M. Hansen +1
False data injection attacks (FDIAs) on smart inverters are a growing concern linked to increased renewable energy production. While data-based FDIA detection methods are also acti…
PINN surrogate of Li-ion battery models for parameter inference. Part II: Regularization and application of the pseudo-2D model
Malik Hassanaly, Peter J. Weddle, Ryan N. King +6
Bayesian parameter inference is useful to improve Li-ion battery diagnostics and can help formulate battery aging models. However, it is computationally intensive and cannot be eas…
PINN surrogate of Li-ion battery models for parameter inference. Part I: Implementation and multi-fidelity hierarchies for the single-particle model
Malik Hassanaly, Peter J. Weddle, Ryan N. King +6
To plan and optimize energy storage demands that account for Li-ion battery aging dynamics, techniques need to be developed to diagnose battery internal states accurately and rapid…
Discovery of False Data Injection Schemes on Frequency Controllers with Reinforcement Learning
Romesh Prasad, Malik Hassanaly, Xiangyu Zhang +1
While inverter-based distributed energy resources (DERs) play a crucial role in integrating renewable energy into the power system, they concurrently diminish the grid's system ine…