Adaptive dual-layer performance guaranteed sliding mode control for VSWT with EWS prediction

Abstract

The maximum power point tracking (MPPT) control is a challenging task in wind power technology due to the stochastic and intermittent nature of the wind speed. This study proposes an effective wind speed (EWS) prediction-assisted adaptive performance guaranteed sliding mode controller to improve power capture efficiency for variable-speed wind turbines (VSWT). First, we propose a novel EWS estimation model based on a broad learning system (BLS). The model is trained on data collected from the existing supervisory control and data acquisition (SCADA) system. Furthermore, a BLS-based EWS prediction approach is developed to compensate for time delays present in the estimated EWS time-series. This prediction model can forecast the EWS in real-time to determine the optimal power reference for MPPT. Moreover, to deal with the uncertainties and external disturbances inherent in wind turbine systems and enhance the transient and steady-state performance of the MPPT control scheme, an error transformation technique-based sliding mode controller is developed. Dual-layer adaptation laws are formulated for the switching gain associated with the proposed sliding mode controller to compensate for the uncertainties appropriately and reduce the chattering phenomena. Finally, the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) software is employed to verify the performance of the proposed approach.