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

Designs

Mohammad-Hassan Khooban¹  Meysam Gheisarnejad²  MeisamJahanshahi Zeitouni³  Ahmad Parvaresh⁴  Saeid-Reza Mohseni⁵  Saber Abrazeh⁶ 

[1] DIGIT, Department of Engineering, Aarhus University, 8200 Aarhus N, Denmark;Department of Electrical Engineering, Islamic Azad University, Najafabad Branch, Najafabad, Esfahan 8514143131, Iran;Electrical & Electronic Engineering Department, Shiraz University of Technology, Shiraz 71557-13876, Iran;Electrical Engineering Department, Shahid Bahonar University of Kerman, Kerman 76169-14111, Iran;Electrical Engineering Department, Sharif University of Technology, Tehran 11365-11155, Iran;School of Electrical & Computer Engineering, Shiraz University, Shiraz 71946-84334, Iran;

关键词: pitch angle control;    DDPG algorithm;    backstepping controller;    digital twin (DT);    DSP;    software-in-loop;   

DOI  :  10.3390/designs4020015

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown