Model Order Reduction for Control and Stability Analysis of Complex Dynamical Systems in a DC Microgrid
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Abstract
This paper introduces an approach to manage high order complex systems by employing reduced-order models, thereby minimizing computational demands and hardware pre-requisites. The technique is implemented on a nonlinear sample system comprising six states, and various model order reduction (MOR) methods are scrutinized. Subsequently, linear quadratic regulators (LQRs) are designed using the reduced-order models to govern the system. These controllers are then assessed on the original system via MATLAB simulations. The findings demonstrate the effectiveness of MOR techniques in ensuring satisfactory performance, along with the feasibility of implementing the LQR controller, designed using the reduced order model, on the original system without requiring modifications.Description
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R. Peykarporsan, T. T. Lie, M. Stommel and J. Watson, "Model Order Reduction for Control and Stability Analysis of Complex Dynamical Systems in a DC Microgrid," 2024 IEEE Power & Energy Society General Meeting (PESGM), Seattle, WA, USA, 2024, pp. 1-5, doi: 10.1109/PESGM51994.2024.10689165.
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