Model predictive control of wind turbines using uncertain LIDAR measurements

Abstract

Abstract-The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined by the effective wind speed on the rotor disc. We take the wind speed as a scheduling variable. The wind speed is measurable ahead of the turbine using LIDARs, therefore, the scheduling variable is known for the entire prediction horizon. By taking the advantage of having future values of the scheduling variable, we simplify state prediction for the MPC. Consequently, the control problem of the nonlinear system is simplified into a quadratic programming. We consider uncertainty in the wind propagation time, which is the traveling time of wind from the LIDAR measurement point to the rotor. An algorithm based on wind speed estimation and measurements from the LIDAR is devised to find an estimate of the delay and compensate for it before it is used in the controller. Comparisons between the MPC with error compensation, the MPC without error compensation and an MPC with re-linearization at each sample point based on wind speed estimation are given. It is shown that with appropriate signal processing techniques, LIDAR measurements improve the performance of the wind turbine controller.

Importance of lidar measurement timing accuracy for wind turbine control

“…INTRODUCTION Wind turbine control can be improved through the use of a turbine-mounted lidar, which measures the wind speed F. Dunne at some chosen distance ahead of a wind turbine, giving advance notice of the approaching wind disturbance [1], [2], [3], [4], [5], [6], [7]. Fig.…”

Fatigue load modeling and control for wind turbines based on hysteresis operators

“…The goal is to control a wind turbine P reducing both power fluctuations and the incurred fatigue, see [10], [11], [27]. However, the inclusion of the fatigue damage given by (7) into (16) is not straightforward, due to the Preisach hysteresis operator.…”

“…In the wind turbine control context, current control methods are based on minimization of certain norms of the stress on different components of the wind turbine, which are hoped to reduce fatigue, but are not a trustful characterization of the damage [10], [11]. Other approaches, such as taking the variance of the stress are not a direct representation of fatigue, as mentioned in [12].…”

Fatigue damage estimation and data‐based control for wind turbines

“…The main idea is to control a wind turbine P reducing both output power fluctuations and the incurred fatigue, see [6,7]. Nevertheless, the damage estimate is given by a hysteretic element H, namely a discretised Preisach operator, which needs to be incorporated into the MPC strategy.…”

“…The motivation for this work is to facilitate the shortcomings of the RFC method, which has an algorithmic non-linear structure, requiring deletions as well as a significant history window, and thus used mainly as a post-processing tool. In the wind turbine control context, current control methods are based on minimisation of certain norms of the stress on different components of the wind turbine, which are expected to reduce fatigue, but are not a reliable characterisation of the damage [6,7]. Other approaches, such as taking the variance of a stress are not a direct representation of fatigue, as mentioned in [8].…”