  In this paper, we develop a distributionally robust model predictive control
framework for the control of wind farms with the goal of power tracking and
mechanical stress reduction of the individual wind turbines. We introduce an
ARMA model to predict the turbulent wind speed, where we merely assume that the
residuals are sub-Gaussian noise with statistics contained in an moment-based
ambiguity set. We employ a recently developed distributionally model predictive
control scheme to ensure constraint satisfaction and recursive feasibility of
the control algorithm. The effectiveness of the approach is demonstrated on a
practical example of five wind turbines in a row.
