activity
20212026
most citedBayesian Error-in-Variables Models for the Identification of Power Networks

1 citations · 1 across the 11 of their papers we have counts for

collaborators

13 papers

eess.SY2026

Data-Driven Predictive Control for Wide-Area Power Oscillation Damping

Giacomo Mastroddi, Jan Poland, Mats Larsson +1

We study damping of inter-area oscillations in transmission grids using voltage-source-converter-based high-voltage direct-current (VSC-HVDC) links. Conventional power oscillation…

eess.SY2025

Model-Free Power System Stability Enhancement with Dissipativity-Based Neural Control

Yifei Wang, Han Wang, Kehao Zhuang +2

The integration of converter-interfaced generation introduces new transient stability challenges to modern power systems. Classical Lyapunov- and scalable passivity-based approache…

eess.SY2025

The Bias of Subspace-based Data-Driven Predictive Control

Keith Moffat, Florian Dörfler, Alessandro Chiuso

This paper quantifies and addresses the bias of subspace-based Data-Driven Predictive Control (DDPC) for linear, time-invariant (LTI) systems. The primary focus is the bias that ar…

eess.SY2025

Grid-Connected, Data-Driven Inverter Control, Theory to Hardware

Sebastian Graf, Keith Moffat, Anurag Mohapatra +2

Grid-connected inverter control is challenging to implement due to the difficulty of obtaining and maintaining an accurate grid model. Direct Data-Driven Predictive Control provide…

eess.SY2025

Recursive-ARX for Grid-Edge Fault Detection

Soufiane El Yaagoubi, Keith Moffat, Eduardo Prieto Araujo +1

Future electrical grids will require new ways to identify faults as inverters are not capable of supplying large fault currents to support existing fault detection methods and beca…

eess.SY2025

A Data-driven Predictive Control Architecture for Train Thermal Energy Management

Ahmed Aboudonia, Johannes Estermann, Keith Moffat +2

We aim to improve the energy efficiency of train climate control architectures, with a focus on a specific class of regional trains operating throughout Switzerland, especially in…

eess.SY2025

Revisiting Power System Stabilizers with Increased Inverter-Based Generation: A Case Study

Jovan Krajacic, Keith Moffat, Gustavo Valverde

As power systems evolve with increasing production from Inverter-Based Resources (IBRs), their underlying dynamics are undergoing significant changes that can jeopardize system ope…

eess.SY2025

PRIME: Fast Primal-Dual Feedback Optimization for Markets with Application to Optimal Power Flow

Nicholas Julian Behr, Mattia Bianchi, Keith Moffat +2

Online Feedback Optimization (OFO) controllers iteratively drive a plant to an optimal operating point that satisfies input and output constraints, relying solely on the input-outp…

math.OC2025

Robust Feedback Optimization with Model Uncertainty: A Regularization Approach

Winnie Chan, Zhiyu He, Keith Moffat +3

Feedback optimization optimizes the steady state of a dynamical system by implementing optimization iterations in closed loop with the plant. It relies on online measurements and l…

eess.SY2025

Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection

Joshua Näf, Keith Moffat, Jaap Eising +1

This paper proposes Select-Data-driven Predictive Control (Select-DPC), a new method for controlling nonlinear systems using output-feedback for which data are available but an exp…

math.OC2024

DeePC-Hunt: Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization

Michael Cummins, Alberto Padoan, Keith Moffat +2

This paper introduces Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization (DeePC-Hunt), a backpropagation-based method for automatic hyperparamete…

eess.SY2024

Power Grid Parameter Estimation Without Phase Measurements: Theory and Empirical Validation

Jean-Sébastien Brouillon, Keith Moffat, Florian Dörfler +1

Reliable integration and operation of renewable distributed energy resources requires accurate distribution grid models. However, obtaining precise models is often prohibitively ex…

eess.SY2021★ 1 cited

Bayesian Error-in-Variables Models for the Identification of Power Networks

Jean-Sébastien Brouillon, Emanuele Fabbiani, Pulkit Nahata +3

The increasing integration of intermittent renewable generation, especially at the distribution level,necessitates advanced planning and optimisation methodologies contingent on th…