A Novel Deep Learning Backstepping Controller-Based Digital Twins Technology for Pitch Angle Control of Variable Speed Wind Turbine

Abstract

This paper proposes a deep deterministic policy gradient (DDPG) based nonlinear integral backstepping (NIB) in combination with model free control (MFC) for pitch angle control of variable speed wind turbine. In particular, the controller has been presented as a digital twin (DT) concept, which is an increasingly growing method in a variety of applications. In DDPG-NIB-MFC, the pitch angle is considered as the control input that depends on the optimal rotor speed, which is usually derived from effective wind speed. The system stability according to the Lyapunov theory can be achieved by the recursive nature of the backstepping theory and the integral action has been used to compensate for the steady-state error. Moreover, due to the nonlinear characteristics of wind turbines, the MFC aims to handle the un-modeled system dynamics and disturbances. The DDPG algorithm with actor-critic structure has been added in the proposed control structure to efficiently and adaptively tune the controller parameters embedded in the NIB controller. Under this effort, a digital twin of a presented controller is defined as a real-time and probabilistic model which is implemented on the digital signal processor (DSP) computing device. To ensure the performance of the proposed approach and output behavior of the system, software-in-loop (SIL) and hardware-in-loop (HIL) testing procedures have been considered. From the simulation and implementation outcomes, it can be concluded that the proposed backstepping controller based DDPG is more effective, robust, and adaptive than the backstepping and proportional-integral (PI) controllers optimized by particle swarm optimization (PSO) in the presence of uncertainties and disturbances.