An Economic Model Predictive Control Approach for Load Mitigation on Multiple Tower Locations of Wind Turbines

TL;DR

This paper presents an economic model predictive control framework for wind turbines that effectively reduces tower loads at multiple locations while maintaining power output, addressing a gap in load mitigation strategies for taller, flexible towers.

Contribution

It introduces a novel eMPC approach incorporating a linear modal model and energy-based turbine dynamics to mitigate loads at multiple tower points without sacrificing power.

Findings

Effective load reduction at multiple tower locations.

Maintains power production levels.

Convex formulation ensures computational efficiency.

Abstract

The current trend in the evolution of wind turbines is to increase their rotor size in order to capture more power. This leads to taller, slender and more flexible towers, which thus experience higher dynamical loads due to the turbine rotation and environmental factors. It is hence compelling to deploy advanced control methods that can dynamically counteract such loads, especially at tower positions that are more prone to develop cracks or corrosion damages. Still, to the best of the authors' knowledge, little to no attention has been paid in the literature to load mitigation at multiple tower locations. Furthermore, there is a need for control schemes that can balance load reduction with optimization of power production. In this paper, we develop an Economic Model Predictive Control (eMPC) framework to address such needs. First, we develop a linear modal model to account for the tower flexural dynamics. Then we incorporate it into an eMPC framework, where the dynamics of the turbine rotation are expressed in energy terms. This allows us to obtain a convex formulation, that is computationally attractive. Our control law is designed to avoid the 'turn-pike' behavior and guarantee recursive feasibility. We demonstrate the performance of the proposed controller on a 5MW reference WT model: the results illustrate that the proposed controller is able to reduce the tower loads at multiple locations, without significant effects to the generated power.