Decentralized Data-Enabled Predictive Control for Power System Oscillation Damping

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 for power system oscillation damping. Conventional is model based. However, practice, detailed accurate parametric models are rarely available. In contrast, the DeePC uses only input-output data measured from unknown predict future trajectories calculate policy. showcase that can effectively attenuate interarea oscillations even presence of measurement noise, communication delays, nonlinear loads, uncertain load fluctuations. investigate performance under different matrix structures as data-driven predictors. Furthermore, we derive Min-Max be applied independently multiple VSC-HVDC mitigate oscillations, which enables decentralized robust control. Further, discuss how relieve computational burden by reducing dimension prediction uncertainty leverage disturbance feedback reduce conservativeness robustification. illustrate our results with high-fidelity, nonlinear, noisy simulations four-area test system.