Multiple Adaptive Model Predictive Controllers for Frequency Regulation in Wind Farms
Haixin Wang, Zihao Yang, Zhe Chen, Jun Liang, Gen Li, Junyou Yang, Shiyan Hu
Abstract
Frequent and inadequate power regulation could significantly impact the main shaft mechanical load and the fatigue of wind turbines, which imposes a stringent requirement to perform frequency regulation. However, the existing work on frequency regulation mainly uses torque compensation to improve the frequency response, while few of them consider the mechanical fatigue of the main shaft caused by torque compensation of the frequency controller. In this paper, the mechanical fatigue of the main shaft can be mitigated in all of the speed sections thanks to the proposed frequency regulation controllers. Precisely, a multiple adaptive model predictive controller (MAMPC), which seamlessly integrates the multiple model predictive control (MMPC) and the real-time AutoRegressive with eXogenous inputs (ARX) model, is proposed. It nicely handles the rate of change in compensation torque to mitigate the mechanical load on the shaft in all of the speed sections. The effectiveness of our method is verified through extensive simulations. With the proposed method, the minimum frequency deviation can be reduced, and the number of fatigue cycles of the main shaft can be extended.