Model predictive control strategy in waked wind farms for optimal fatigue loads

TL;DR

This paper presents a model predictive control strategy for wind farms that optimizes power tracking and reduces fatigue loads, balancing load distribution among turbines without compromising overall power performance.

Contribution

It introduces a closed-loop MPC approach that jointly minimizes tracking errors, fatigue loads, and load imbalance, demonstrating effectiveness in a 64-turbine simulation case.

Findings

The control reduces dynamical fatigue loads effectively.

Power tracking performance remains unaffected.

Load imbalance among turbines can be minimized with proper penalty tuning.

Abstract

With the rapid growth of wind power penetration, wind farms (WFs) are required to implement frequency regulation that active power control to track a given power reference. Due to the wake interaction of the wind turbines (WTs), there is more than one solution to distributing power reference among the operating WTs, which can be exploited as an optimization problem for the second goal, such as fatigue load alleviation. In this paper, a closed-loop model predictive controller is developed that minimizes the wind farm tracking errors, the dynamical fatigue load, and and the load equalization. The controller is evaluated in a mediumfidelity model. A 64 WTs simulation case study is used to demonstrate the control performance for different penalty factor settings. The results indicated the WF can alleviate dynamical fatigue load and have no significant impact on power tracking. However, the uneven load distribution in the wind turbine system poses challenges for maintenance. By adding a trade-off between the load equalization and dynamical fatigue load, the load differences between WTs are significantly reduced, while the dynamical fatigue load slightly increases when selecting a proper penalty factor.