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• W3039035035 abstract "We employ a novel data-enabled predictive control (DeePC) algorithm in voltage source converter (VSC) based high-voltage DC (HVDC) stations to perform safe and optimal wide-area control for power system oscillation damping. Conventional optimal wide-area control is model-based. However, in practice detailed and accurate parametric power system models are rarely available. In contrast, the DeePC algorithm uses only input/output data measured from the unknown system to predict the future trajectories and calculate the optimal control policy. We showcase that the DeePC algorithm can effectively attenuate inter-area oscillations even in the presence of measurement noise, communication delays, nonlinear loads and uncertain load fluctuations. We investigate the performance under different matrix structures as data-driven predictors. Furthermore, we derive a novel Min-Max DeePC algorithm to be applied independently in multiple VSC-HVDC stations to mitigate inter-area oscillations, which enables decentralized and robust optimal wide-area control. Further, we discuss how to relieve the computational burden of the Min-Max DeePC by reducing the dimension of prediction uncertainty and how to leverage disturbance feedback to reduce the conservativeness of robustification. We illustrate our results with high-fidelity, nonlinear, and noisy simulations of a four-area test system." @default.
• W3039035035 created "2020-07-10" @default.
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• W3039035035 date "2019-11-27" @default.
• W3039035035 modified "2023-10-18" @default.
• W3039035035 title "Decentralized Data-Enabled Predictive Control for Power System Oscillation Damping" @default.
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• W3039035035 doi "https://doi.org/10.48550/arxiv.1911.12151" @default.
• W3039035035 hasPublicationYear "2019" @default.
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