  Recently, subsynchronous oscillations (SSOs) have emerged frequently
worldwide, with the high penetration of renewable power generation in modern
power systems. The SSO introduced by renewables has become a prominent new
stability problem, seriously threatening the stable operation of systems. This
paper proposes a data-driven dynamic optimal controller for renewable energy
integrated power systems, to suppress SSOs with the control of renewables. The
challenges of the controller design are the nonlinearity, complexity and hard
accessibility of the system models. Using Koopman operator, the system dynamics
are accurately extracted from data and utilized to the linear model predictive
control (MPC). Firstly, the globally linear representation of the system
dynamics is obtained by lifting, and the key states are selected as control
signals by analyzing Koopman participation factors. Subsequently, augmented
with the control term, the Koopman linear parameter-varying predictor of the
controlled system is constructed. Finally, using MPC, the proposed controller
computes control signals online in a moving horizon fashion. Case studies show
that the proposed controller is effective, adaptive and robust in various
conditions, surpassing other controllers with reliable control performance.
