Model predictive controllers for reduction of mechanical fatigue in wind farms

TL;DR

This paper introduces MPC-based dispatching algorithms for wind farms that reduce mechanical fatigue by incorporating wind turbulence prediction, showing improved stress management in large-scale offshore turbines.

Contribution

It presents novel MPC algorithms with wind turbulence modeling, enabling stress reduction without hardware changes, validated through simulations on large offshore wind farms.

Findings

MPC controllers reduce mechanical stresses in wind turbines.

Inclusion of wind turbulence prediction improves control accuracy.

Simulations demonstrate effectiveness on large offshore wind farms.

Abstract

We consider the problem of dispatching WindFarm (WF) power demand to individual Wind Turbines (WT) with the goal of minimizing mechanical stresses. We assume wind is strong enough to let each WTs to produce the required power and propose different closed-loop Model Predictive Control (MPC) dispatching algorithms. Similarly to existing approaches based on MPC, our methods do not require changes in WT hardware but only software changes in the SCADA system of the WF. However, differently from previous MPC schemes, we augment the model of a WT with an ARMA predictor of the wind turbulence, which reduces uncertainty in wind predictions over the MPC control horizon. This allows us to develop both stochastic and deterministic MPC algorithms. In order to compare different MPC schemes and demonstrate improvements with respect to classic open-loop schedulers, we performed simulations using the SimWindFarm toolbox for MatLab. We demonstrate that MPC controllers allow to achieve reduction of stresses even in the case of large installations such as the 100-WTs Thanet offshore WF.