Publication details


S. Riverso, S. Mancini, F. Sarzo, and G. Ferrari-Trecate. Deterministic and stochastic mpc algorithms for minimizing mechanical stresses in wind farms. Proc. 54th IEEE Conference on Decision and Control , pages 1340--1345, DOI: 10.1109/CDC.2015.7402397, 2015. Osaka, Japan, December 15-18.


We consider the problem of dispatching WindFarm(WF) power demand to individual Wind Turbines (WTs)with the goal of minimizing mechanical stresses. We assumewind is strong enough to let each WT produce the requiredpower and propose different closed-loop Model PredictiveControl (MPC) dispatching algorithms. Similarly to existingapproaches based on MPC, our methods do not require changesin WT hardware but only software changes in the SCADAsystem of the WF. However, differently from other MPCschemes, we augment the model of a WT with an ARMApredictor of the wind turbulence, which reduces uncertaintyin wind predictions over the MPC control horizon. Thisallows us to develop both stochastic and deterministic MPCalgorithms. In order to compare different MPC schemes anddemonstrate improvements over classic open-loop schedulers,we use simulations based on the SimWindFarm toolbox forMatLab.