Model Predictive Control With a Cascaded Hammerstein Neural Network of a Wind Turbine Providing Frequency Containment Reserve

Abstract

This article presents an application of neural network-based Model Predictive Control (MPC) to improve the wind turbine control system’s performance in providing frequency control ancillary services to the grid. A closed-loop Hammerstein structure is used to approximate the behavior of a 5MW floating offshore wind turbine with a Permanent Magnet Synchronous Generator (PMSG). The multilayer perceptron neural networks estimate the aerodynamic behavior of the nonlinear steady-state part, and the linear AutoRegressive with Exogenous input (ARX) is applied to identify the linear time-invariant dynamic part. Using the specific structure of the Cascade Hammerstein design simplifies the online linearization at each operating point. The proposed algorithm evades the necessity of nonlinear optimization and uses quadratic programming to obtain control actions. Eventually, the proposed control design provides a fast and stable response to the grid frequency variations with optimal pitch and torque cooperation. The performance of the MPC is compared with the gain-scheduled proportional-integral (PI) controller. Results demonstrate the effectiveness of the designed control system in providing Frequency Containment Reserve (FCR) and frequency regulation in the future of power systems.