arXiv subjects

Keith Moffat

Publications and source records attributed to Keith Moffat.

15 recordsLinked to original sources

arXiv2026

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

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 damping controllers rely on system models that are difficult to obtain in practice. Data-driven Predictive Control (DPC) addresses this limitation by replacing explicit models with data. We apply AutoRegressive with eXogenous inputs (ARX)-based predictive control and its Transient Predictive Control (TPC) variant, and compare them with Data-enabled Predictive Control (DeePC) and two standard model-based controllers. The methods are evaluated in simulation on a system exhibiting both inter-area and local oscillation modes. ARX-based predictive control and DeePC both achieve effective damping, while the ARX-based methods require less online computation. Using warm-started, pre-factorized operator-splitting solvers, ARX/TPC control actions are computed in less than 1ms. These results demonstrate that DPC is a viable approach for power-system oscillation damping for the given test case.

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arXiv2025

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

The integration of converter-interfaced generation introduces new transient stability challenges to modern power systems. Classical Lyapunov- and scalable passivity-based approaches typically rely on restrictive assumptions, and finding storage functions for large grids is generally considered intractable. Furthermore, most methods require an accurate grid dynamics model. To address these challenges, we propose a model-free, nonlinear, and dissipativity-based controller which, when applied to grid-connected virtual synchronous generators (VSGs), enhances power system transient stability. Using input-state data, we train neural networks to learn dissipativity-characterizing matrices that yield stabilizing controllers. Furthermore, we incorporate cost function shaping to improve the performance with respect to the user-specified objectives. Numerical results on a modified, all-VSG Kundur two-area power system validate the effectiveness of the proposed approach.

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arXiv2025

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

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 provides a model-free alternative to traditional model-based control methods. This paper describes how the recently-proposed Transient Predictive Control (TPC) can be used for real-world, plug-and-play inverter control. The following hypotheses were tested: 1) The TPC algorithm can be run online using standard hardware, and 2) TPC, which is derived using Linear Time-Invariant assumptions, is effective for grid-connected inverter control, which is a nonlinear and time-varying system. Experiments conducted on a two-converter benchtop setup and at the CoSES Laboratory on a 25 kVA converter connected to the Munich grid support these hypotheses.

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arXiv2025

The Bias of Subspace-based Data-Driven Predictive Control

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 arises when the training data is gathered with a feedback controller in closed-loop with the system. First, the closed-loop bias of Subspace Predictive Control is quantified using the training data innovations. Next, the bias of direct, subspace-based DDPC methods DeePC and $\gamma$-DDPC is shown to consist of two parts--the Subspace Bias, which arises from closed-loop data, and an Optimism Bias, which arises from DeePC/$\gamma$-DDPC's "optimistic" adjustment of the output trajectory. We show that, unlike subspace-based DDPC methods, Transient Predictive Control does not suffer from Subspace Bias or Optimism Bias. Double integrator experiments demonstrate that Subspace and Optimism Bias are responsible for poor reference tracking by the subspace-based DDPC methods.

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arXiv2025

Recursive-ARX for Grid-Edge Fault Detection

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 because distributed resources may feed faults from the edge of the grid. This paper proposes the use of real-time system identification for online power-system fault detection. Specifically, we implement Recursive ARX (rARX) system identification on a grid-connected inverter. Experiments demonstrate that the proposed rARX method is able to both detect large faults quickly, and distinguish between high-impedance faults and large load increases. These results indicate that rARX grid-edge fault detection is a promising research direction for improving the reliability and safety of modern electric grids.

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arXiv2025

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

As power systems evolve with increasing production from Inverter-Based Resources (IBRs), their underlying dynamics are undergoing significant changes that can jeopardize system operation, leading to poorly damped oscillations or small-signal rotor angle instability. In this work, we investigate whether Power System Stabilizer (PSS) setting adjustments can effectively restore system stability and provide adequate damping in systems with increased IBR penetration, using the benchmark Kundur Two-Area System as a case study. Specifically, we evaluate the model-based Residues and P-Vref PSS tuning methods to examine their effectiveness under evolving grid conditions. Our findings indicate that the effectiveness of these tuning methods is not guaranteed, particularly when coordination is limited. Consequently, our case study motivates local and adaptive online PSS tuning methods.

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arXiv2025

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

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 Zurich and Geneva. Heating, Ventilation, and Air Conditioning (HVAC) systems represent the second largest energy consumer in these trains after traction. The current architecture comprises a high-level rule-based controller and a low-level tracking controller. To improve train energy efficiency, we propose adding a middle data-driven predictive control layer aimed at minimizing HVAC energy consumption while maintaining passenger comfort. The scheme incorporates a multistep prediction model developed using real-world data collected from a limited number of train coaches. To validate the effectiveness of the proposed architecture, we conduct multiple experiments on a separate set of train coaches; our results suggest energy savings between 10% and 35% with respect to the current architecture.

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arXiv2025

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

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-output sensitivity as model information. This paper introduces PRIME (PRoximal Iterative MarkEts), a novel OFO approach based on proximal-point iterations. Unlike existing OFO solutions, PRIME admits a market-based implementation, where self-interested actors are incentivized to make choices that result in safe and efficient operation, without communicating private costs or constraints. Furthermore, PRIME can handle non-smooth objective functions, achieve fast convergence rates and rapid constraint satisfaction, and effectively reject measurement noise. We demonstrate PRIME on an AC optimal power flow problem, obtaining an efficient real-time nonlinear local marginal pricing scheme.

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arXiv2025

Robust Feedback Optimization with Model Uncertainty: A Regularization Approach

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 limited model information, namely, the input-output sensitivity. In practice, various issues including inaccurate modeling, lack of observation, or changing conditions can lead to sensitivity mismatches, causing closed-loop sub-optimality or even instability. To handle such uncertainties, we pursue robust feedback optimization, where we optimize the closed-loop performance against all possible sensitivities lying in specific uncertainty sets. We provide tractable reformulations for the corresponding min-max problems via regularizations and characterize the online closed-loop performance through the tracking error in case of time-varying optimal solutions. Simulations on a distribution grid illustrate the effectiveness of our robust feedback optimization controller in addressing sensitivity mismatches in a non-stationary environment.

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arXiv2025

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

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 explicit model is not. At each timestep, Select-DPC employs only the most relevant data to implicitly linearize the dynamics in "trajectory space". Then, taking user-defined output constraints into account, it makes control decisions using a convex optimization. This optimal control is applied in a receding-horizon manner. As the online data-selection is the core of Select-DPC, we propose and verify both norm-based and manifold-embedding-based selection methods. We evaluate Select-DPC on three benchmark nonlinear system simulators -- rocket-landing, a robotic arm and cart-pole inverted pendulum swing-up -- comparing them with standard Data-enabled Predictive Control (DeePC) and Time-Windowed DeePC methods, and find that Select-DPC outperforms both methods.

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arXiv2024

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

This paper introduces Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization (DeePC-Hunt), a backpropagation-based method for automatic hyperparameter tuning of the DeePC algorithm. The necessity for such a method arises from the importance of hyperparameter selection to achieve satisfactory closed-loop DeePC performance. The standard methods for hyperparameter selection are to either optimize the open-loop performance, or use manual guess-and-check. Optimizing the open-loop performance can result in unacceptable closed-loop behavior, while manual guess-and-check can pose safety challenges. DeePC-Hunt provides an alternative method for hyperparameter tuning which uses an approximate model of the system dynamics and backpropagation to directly optimize hyperparameters for the closed-loop DeePC performance. Numerical simulations demonstrate the effectiveness of DeePC in combination with DeePC-Hunt in a complex stabilization task for a nonlinear system and its superiority over model-based control strategies in terms of robustness to model misspecifications.

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arXiv2024

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

Reliable integration and operation of renewable distributed energy resources requires accurate distribution grid models. However, obtaining precise models is often prohibitively expensive, given their large scale and the ongoing nature of grid operations. To address this challenge, considerable efforts have been devoted to harnessing abundant consumption data for automatic model inference. The primary result of the paper is that, while the impedance of a line or a network can be estimated without synchronized phase angle measurements in a consistent way, the admittance cannot. Furthermore, a detailed statistical analysis is presented, quantifying the expected estimation errors of four prevalent admittance estimation methods. Such errors constitute fundamental model inference limitations that cannot be resolved with more data. These findings are empirically validated using synthetic data and real measurements from the town of Walenstadt, Switzerland, confirming the theory. The results contribute to our understanding of grid estimation limitations and uncertainties, offering guidance for both practitioners and researchers in the pursuit of more reliable and cost-effective solutions.

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arXiv2022

Robust online joint state/input/parameter estimation of linear systems

This paper presents a method for jointly estimating the state, input, and parameters of linear systems in an online fashion. The method is specially designed for measurements that are corrupted with non-Gaussian noise or outliers, which are commonly found in engineering applications. In particular, it combines recursive, alternating, and iteratively-reweighted least squares into a single, one-step algorithm, which solves the estimation problem online and benefits from the robustness of least-deviation regression methods. The convergence of the iterative method is formally guaranteed. Numerical experiments show the good performance of the estimation algorithm in presence of outliers and in comparison to state-of-the-art methods.

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arXiv2022

Using Voltage Phasor Control to Avoid Distribution Network Constraint Violations

In this paper, we introduce Voltage Phasor Control (VPC), also known as Phasor Based Control, as a novel way of implementing Optimal Power Flow (OPF). Unlike conventional OPF, in which the power flow optimization broadcasts power injections, the VPC power flow optimization broadcasts voltage phasor setpoints to feedback controllers distributed throughout the network which respond to disturbances in real time. In this paper, we demonstrate that VPC can actively manage distributed energy resources to avoid voltage magnitude and line flow constraint violations in power distribution networks. We provide sensitivities and bounds that quantify how the distributed voltage phasor feedback control reduces the effect of disturbances on the network voltages and line flows. Using simulations, we compare the performance of VPC with the related but more conventional Voltage Magnitude Control (VMC). The sensitivities, bounds, and simulations provide implementation insights, which we highlight, for how distribution network operators can use VPC and VMC to avoid constraint violations.

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arXiv2021

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

The increasing integration of intermittent renewable generation, especially at the distribution level,necessitates advanced planning and optimisation methodologies contingent on the knowledge of thegrid, specifically the admittance matrix capturing the topology and line parameters of an electricnetwork. However, a reliable estimate of the admittance matrix may either be missing or quicklybecome obsolete for temporally varying grids. In this work, we propose a data-driven identificationmethod utilising voltage and current measurements collected from micro-PMUs. More precisely,we first present a maximum likelihood approach and then move towards a Bayesian framework,leveraging the principles of maximum a posteriori estimation. In contrast with most existing con-tributions, our approach not only factors in measurement noise on both voltage and current data,but is also capable of exploiting available a priori information such as sparsity patterns and knownline parameters. Simulations conducted on benchmark cases demonstrate that, compared to otheralgorithms, our method can achieve significantly greater accuracy.

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