Data-driven MPC of descriptor systems: A case study for power networks
Philipp Schmitz, Alexander Engelmann, Timm Faulwasser, Karl Worthmann
Abstract
Recently, data-driven predictive control of linear systems has received wide-spread research attention. It hinges on the fundamental lemma by Willems et al. In a previous paper, we have shown how this framework can be applied to predictive control of linear time-invariant descriptor systems. In the present paper, we present a case study wherein we apply data-driven predictive control to a discrete-time descriptor model obtained by discretization of the power-swing equations for a nine-bus system. Our results show the efficacy of the proposed control scheme and they underpin the prospect of the data-driven framework for control of descriptor systems.
Topics & Concepts
Model predictive controlDiscretizationControl theory (sociology)Predictive powerLTI system theoryComputer scienceLinear systemInvariant (physics)Electric power systemControl (management)Power (physics)Artificial intelligenceMathematicsQuantum mechanicsMathematical analysisMathematical physicsPhysicsPhilosophyEpistemologyMicrogrid Control and OptimizationAdvanced Control Systems OptimizationPower System Optimization and Stability