Applications of physics-informed neural networks in power systems-a review
The advances of deep learning (DL) techniques bring new opportunities to numerous
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …
DeSKO: Stability-assured robust control with a deep stochastic Koopman operator
M Han, J Euler-Rolle… - … Conference on Learning …, 2021 - openreview.net
The Koopman operator theory linearly describes nonlinear dynamical systems in a high-
dimensional functional space and it allows to apply linear control methods to highly …
dimensional functional space and it allows to apply linear control methods to highly …
Robust learning-based control for uncertain nonlinear systems with validation on a soft robot
Existing modeling and control methods for real-world systems typically deal with uncertainty
and nonlinearity on a case-by-case basis. We present a universal and robust control …
and nonlinearity on a case-by-case basis. We present a universal and robust control …
Koopman-based differentiable predictive control for the dynamics-aware economic dispatch problem
The dynamics-aware economic dispatch (DED) problem embeds low-level generator
dynamics and operational constraints to enable near real-time scheduling of generation …
dynamics and operational constraints to enable near real-time scheduling of generation …
Solving the dynamics-aware economic dispatch problem with the koopman operator
E King, C Bakker, A Bhattacharya… - Proceedings of the …, 2021 - dl.acm.org
The dynamics-aware economic dispatch (DED) problem embeds low-level generator
dynamics and operational constraints to enable near real-time scheduling of generation …
dynamics and operational constraints to enable near real-time scheduling of generation …
Multi-level optimization with the koopman operator for data-driven, domain-aware, and dynamic system security
Abstract Cyber–Physical Systems (CPSs) like the power grid are critically important but also
increasingly vulnerable; ensuring reliable system operation in the face of disruptions is …
increasingly vulnerable; ensuring reliable system operation in the face of disruptions is …
Data-driven resilience characterization of control dynamical systems
S Sinha, SP Nandanoori… - 2022 American …, 2022 - ieeexplore.ieee.org
In this paper, we define and quantify resilience of a power network and propose data-driven
algorithms for computing the same for the power grid. To do this, we use the Koopman …
algorithms for computing the same for the power grid. To do this, we use the Koopman …
Data-driven characterization of recovery energy in controlled dynamical systems using koopman operator
T Ramachandran, SP Nandanoori… - 2022 IEEE 61st …, 2022 - ieeexplore.ieee.org
A key aspect of a resilient dynamical system is its ability to recover from large disruptions. In
this paper, we define and characterize recovery energy as a quantitative measure of the …
this paper, we define and characterize recovery energy as a quantitative measure of the …
Koopman Dynamic Modeling for Global and Unified Representations of Rigid Body Systems Making and Breaking Contact
A global modeling methodology based on Koopman operator theory for the dynamics of
rigid bodies that make and break contact is presented. Traditionally, robotic systems that …
rigid bodies that make and break contact is presented. Traditionally, robotic systems that …
Deception-based cyber attacks on hierarchical control systems using domain-aware koopman learning
Industrial control systems are subject to cyber attacks that produce physical consequences.
These attacks can be both hard to detect and protracted. Here, we focus on deception-based …
These attacks can be both hard to detect and protracted. Here, we focus on deception-based …