Decentralized Data-Enabled Predictive Control for Power System Oscillation Damping

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 interarea 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 interarea 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.