44 citations · 250 across the 61 of their papers we have counts for
9 papers · 1 filter
Voltage-Dependent Electromechanical Wave Propagation Modeling for Dynamic Stability Analysis in Power Systems
Somayeh Yarahmadi, Daniel Adrian Maldonado, Lamine Mili +2
Accurate dynamic modeling of power systems is essential to assess the stability of electrical power systems when faced with disturbances, which can trigger cascading failures leadi…
Centralized calibration of power system dynamic models using variational data assimilation
Ahmed Attia, D. Adrian Maldonado, Emil Constantinescu +1
This paper presents a novel centralized, variational data assimilation approach for calibrating transient dynamic models in electrical power systems, focusing on load model paramet…
Accelerating Optimal Power Flow with GPUs: SIMD Abstraction of Nonlinear Programs and Condensed-Space Interior-Point Methods
Sungho Shin, François Pacaud, Mihai Anitescu
This paper introduces a framework for solving alternating current optimal power flow (ACOPF) problems using graphics processing units (GPUs). While GPUs have demonstrated remarkabl…
Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field
Lele Luan, Nesar Ramachandra, Sandipp Krishnan Ravi +6
Modern computational methods, involving highly sophisticated mathematical formulations, enable several tasks like modeling complex physical phenomenon, predicting key properties an…
Network Cascade Vulnerability using Constrained Bayesian Optimization
Albert Lam, Mihai Anitescu, Anirudh Subramanyam
Measures of power grid vulnerability are often assessed by the amount of damage an adversary can exact on the network. However, the cascading impact of such attacks is often overlo…
Scalable Physics-based Maximum Likelihood Estimation using Hierarchical Matrices
Yian Chen, Mihai Anitescu
Physics-based covariance models provide a systematic way to construct covariance models that are consistent with the underlying physical laws in Gaussian process analysis. The unkn…
Efficient Computation of Power System Maximum Transient Linear Growth
Daniel Adrian Maldonado, Emil Constantinescu, Junbo Zhao +1
Existing methods to determine the stability of a power system to small perturbations are based on eigenvalue analysis and focus on the asymptotic (long-term) behavior of the power…
Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
Zehao Niu, Mihai Anitescu, Jie Chen
Gaussian processes (GPs) are an attractive class of machine learning models because of their simplicity and flexibility as building blocks of more complex Bayesian models. Meanwhil…
Parallel Interior-Point Solver for Block-Structured Nonlinear Programs on SIMD/GPU Architectures
François Pacaud, Michel Schanen, Sungho Shin +2
We investigate how to port the standard interior-point method to new exascale architectures for block-structured nonlinear programs with state equations. Computationally, we decomp…