8 citations · 16 across the 13 of their papers we have counts for
13 papers
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…
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…
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…
Learning the Evolution of Correlated Stochastic Power System Dynamics
Tyler E. Maltba, Vishwas Rao, Daniel Adrian Maldonado
A machine learning technique is proposed for quantifying uncertainty in power system dynamics with spatiotemporally correlated stochastic forcing. We learn one-dimensional linear p…
Randomized Block Adaptive Linear System Solvers
Vivak Patel, Mohammad Jahangoshahi, Daniel Adrian Maldonado
Randomized linear solvers randomly compress and solve a linear system with compelling theoretical convergence rates and computational complexities. However, such solvers suffer a s…
Condensed interior-point methods: porting reduced-space approaches on GPU hardware
François Pacaud, Sungho Shin, Michel Schanen +2
The interior-point method (IPM) has become the workhorse method for nonlinear programming. The performance of IPM is directly related to the linear solver employed to factorize the…
Batched Second-Order Adjoint Sensitivity for Reduced Space Methods
François Pacaud, Michel Schanen, Daniel Adrian Maldonado +4
This paper presents an efficient method for extracting the second-order sensitivities from a system of implicit nonlinear equations on upcoming graphical processing units (GPU) dom…
A Feasible Reduced Space Method for Real-Time Optimal Power Flow
François Pacaud, Daniel Adrian Maldonado, Sungho Shin +2
We propose a novel feasible-path algorithm to solve the optimal power flow (OPF) problem for real-time use cases. The method augments the seminal work of Dommel and Tinney with sec…
Trust-region approximation of extreme trajectories in power system dynamics
Daniel Adrian Maldonado, Emil Constantinescu, Hong Zhang +2
In this work we present a novel technique, based on a trust-region optimization algorithm and second-order trajectory sensitivities, to compute the extreme trajectories of power sy…
Convergence of Adaptive, Randomized, Iterative Linear Solvers
Vivak Patel, Mohammad Jahangoshahi, Daniel Adrian Maldonado
Deterministic and randomized, row-action and column-action linear solvers have become increasingly popular owing to their simplicity, low computational and memory complexities, and…
Sequential Bayesian Parameter Estimation of Stochastic Dynamic Load Models
Daniel Adrian Maldonado, Vishwas Rao, Mihai Anitescu +1
In this paper we focus on the parameter estimation of dynamic load models with stochastic terms, in particular, load models where protection settings are uncertain, such as in aggr…
Probabilistic analysis of masked loads with aggregated photovoltaic production
Shaohui Liu, Daniel Adrian Maldonado, Emil M. Constantinescu
In this paper we present a probabilistic analysis framework to estimate behind-the-meter photovoltaic generation in real time. We develop a forward model consisting of a spatiotemp…
An Implicit Representation and Iterative Solution of Randomly Sketched Linear Systems
Vivak Patel, Mohammad Jahangoshahi, Daniel Adrian Maldonado
Randomized linear system solvers have become popular as they have the potential to reduce floating point complexity while still achieving desirable convergence rates. One particula…