4 citations · 7 across the 5 of their papers we have counts for
5 papers
Efficient Bayesian calibration of many-parameter system models
Promit Chakroborty, Sankaran Mahadevan
Computer models of complex engineering systems rely on proper tuning of their model parameters to ensure accurate predictions of the system behavior. The challenge of effectively c…
DAE-Aware Bayesian Inference for Joint Generator-Network Parameter Estimation
Abdallah Alalem Albustami, Ahmad F. Taha, Sankaran Mahadevan
This paper addresses the classic problem of parameter estimation (PE) in multimachine power system models. Such models are typically described by a set of nonlinear differential-al…
Graph neural networks for power grid operational risk assessment under evolving grid topology
Yadong Zhang, Pranav M Karve, Sankaran Mahadevan
This article investigates the ability of graph neural networks (GNNs) to identify risky conditions in a power grid over the subsequent few hours, without explicit, high-resolution…
Power grid operational risk assessment using graph neural network surrogates
Yadong Zhang, Pranav M Karve, Sankaran Mahadevan
We investigate the utility of graph neural networks (GNNs) as proxies of power grid operational decision-making algorithms (optimal power flow (OPF) and security-constrained unit c…
Operational risk quantification of power grids using graph neural network surrogates of the DC OPF
Yadong Zhang, Pranav M Karve, Sankaran Mahadevan
A DC OPF surrogate modeling framework is developed for Monte Carlo (MC) sampling-based risk quantification in power grid operation. MC simulation necessitates solving a large numbe…