Zobrazeno 1 - 10
of 392
pro vyhledávání: '"Anitescu, Mihai"'
Data-driven and adaptive control approaches face the problem of introducing sudden distributional shifts beyond the distribution of data encountered during learning. Therefore, they are prone to invalidating the very assumptions used in their own con
Externí odkaz:
http://arxiv.org/abs/2409.11549
The statistical spread of transmission outages on a fast protection time scale based on utility data
When there is a fault, the protection system automatically removes one or more transmission lines on a fast time scale of less than one minute. The outaged lines form a pattern in the transmission network. We extract these patterns from utility outag
Externí odkaz:
http://arxiv.org/abs/2407.15059
Autor:
Rossmann, Ramsey, Anitescu, Mihai, Bessac, Julie, Ferris, Michael, Krock, Mitchell, Luedtke, James, Roald, Line
Power grid expansion planning requires making large investment decisions in the present that will impact the future cost and reliability of a system exposed to wide-ranging uncertainties. Extreme temperatures can pose significant challenges to provid
Externí odkaz:
http://arxiv.org/abs/2405.18538
This paper explores two condensed-space interior-point methods to efficiently solve large-scale nonlinear programs on graphics processing units (GPUs). The interior-point method solves a sequence of symmetric indefinite linear systems, or Karush-Kuhn
Externí odkaz:
http://arxiv.org/abs/2405.14236
This paper demonstrates the scalability of open-source GPU-accelerated nonlinear programming (NLP) frameworks -- ExaModels.jl and MadNLP.jl -- for solving multi-period alternating current (AC) optimal power flow (OPF) problems on GPUs with high memor
Externí odkaz:
http://arxiv.org/abs/2405.14032
Autor:
Ramadan, Mohammad S., Anitescu, Mihai
The theory of dual control was introduced more than seven decades ago. Although it has provided rich insights to the fields of control, estimation, and system identification, dual control is generally computationally prohibitive. In recent years, how
Externí odkaz:
http://arxiv.org/abs/2402.18554
In this paper, we address the challenge of solving large-scale graph-structured nonlinear programs (gsNLPs) in a scalable manner. GsNLPs are problems in which the objective and constraint functions are associated with nodes on a graph and depend on t
Externí odkaz:
http://arxiv.org/abs/2402.17170
We propose a data-driven approach for propagating uncertainty in stochastic power grid simulations and apply it to the estimation of transmission line failure probabilities. A reduced-order equation governing the evolution of the observed line energy
Externí odkaz:
http://arxiv.org/abs/2401.02555
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 leading to blackouts. A continuum model proves to be effective in capturing
Externí odkaz:
http://arxiv.org/abs/2311.12326
This paper presents a novel centralized, variational data assimilation approach for calibrating transient dynamic models in electrical power systems, focusing on load model parameters. With the increasing importance of inverter-based resources, asses
Externí odkaz:
http://arxiv.org/abs/2311.07676