Online prediction and optimal control method for subsynchronous oscillation of wind power based on an interpretable surrogate model for machine learning

With the rapid development of new energy power generation, the problem of Subsynchronous Oscillation (SSO) caused by the interaction of large-scale windfarms with the power system has become increasingly prominent. It is of great significance to predict SSO and adopt preventive control measures. In this paper, an online prediction and optimal control method for SSO of wind power grid-connected systems based on an interpretable surrogate model for machine learning is proposed. The Prony algorithm is used to analyze the small disturbance process of a power system to identify the damping level. An evaluation method for system damping based on a gradient boosting decision tree model is established. An optimal control auxiliary decision-making method based on the interpretable surrogate model is proposed. Simulation experiments are conducted on a wind power grid-connected system with multiple Direct-Drive Permanent Synchronous Generators (D-PMSGs) built in Matlab Simulink to verify the effectiveness of the proposed method for online prediction and optimal control of SSO. This could suppress the SSO and improve the stability of the system. Compared with the traditional suppression method, the proposed method does not rely on a detailed model of the wind power grid-connected system while regulating the wind farm pertinently, and the effect of the control measures can be estimated. © 2021 Power System Protection and Control Press.