Data-driven subsynchronous oscillation suppression for renewable energy integrated power systems based on Koopman operator

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 control terms, the Koopman linear parameter-varying predictor of the nonlinear controlled system is constructed, acting as a more accurate predictor. Finally, using the computationally efficient linear MPC within the lifted Koopman space, the proposed controller computes control signals online in a moving horizon fashion. Case studies show that the proposed fully data-driven SSO suppression controller is effective, adaptive and robust in various conditions, surpassing other controllers with reliable control performance.