Robust model predictive control of a wind turbine

2013 American Control Conference

Soltani

Poulsen

et al. 2013

Self Cite

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.

Section: Introductionmentioning

confidence: 99%

Model predictive control of wind turbines using uncertain LIDAR measurements

2013 American Control Conference

Soltani

Poulsen

et al. 2013

Self Cite

“…A better way for measuring the effective wind speed is to use the whole rotor as a measurement device. A method based on Extended Kalman filter is presented in (Mirzaei et al, 2012). In this section another method for estimating the effective wind speed for large wind turbines is presented.…”

Section: Wind Speed Estimationmentioning

confidence: 99%

“…The wind farm consists of nonlinear model of a number of 5MW reference wind turbines (Jonkman et al, 2009). Details of modeling and the nonlinear model is given in (Mirzaei et al, 2012). Each of the turbines are augmented with a PI controller which is designed for controlling and de-rating the turbine.…”

Section: Wind Farm Modelmentioning

confidence: 99%

Estimation of the Possible Power of a Wind Farm

IFAC Proceedings Volumes

Göçmen

Giebel

et al. 2014

Self Cite

It seems possible to increase competitiveness of wind power plants by offering grid services (also called ancillary services) and enter the wind power plants into the ancillary market. One of the ancillary services is called reserve power, the differential capacity between the generated power and the available power in the farm. The total amount of energy that a wind farm can potentially generate is called possible power. It is very important for a wind farm owner to have a relatively accurate estimate of the possible power of the wind farm in order to be able to trade the reserve power. In this paper the possible power calculated based on the estimated effective wind speed of a down regulated wind farm (the industry standard) is compared against the calculated possible power based on the algorithm presented in the paper. The latter takes into account the effect of the wakes of down regulated turbines and therefore gives a more accurate measure of the possible power. It is shown that for an interval of wind speeds the difference between these two can increase the uncertainty in the estimate of the possible power of the down regulated wind farm.

“…Uncertainty in the measured wind speed and also in pitch actuator leads to uncertainty in the B matrix, yet the A matrix is known with enough accuracy. For more details on the uncertain state space model see (Mirzaei et al [2012b]). Collecting all the discussed models, matrices of the state space model become:…”

Section: Modelingmentioning

confidence: 99%

Robust Model Predictive Control of a Nonlinear System with Known Scheduling Variable and Uncertain Gain

IFAC Proceedings Volumes

Poulsen

Niemann

2012

Self Cite

Robust model predictive control (RMPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Because of the special structure of the problem, uncertainty is only in the B matrix (gain) of the state space model. Therefore by taking advantage of this structure, we formulate a tractable minimax optimization problem to solve robust model predictive control problem. Wind turbine is chosen as the case study and we choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon.