  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.
