A Real-time MHE and NMPC Scheme for Wind Turbine Control

Among the several problems arising in the Airborne Wind Energy paradigm, an essential one is the control of the tethered airfoil trajectory during power generation. Tethered flight is a fast, strongly nonlinear, unstable and constrained process, motivating control approaches based on fast Non-linear Model Predictive Control. In this paper, a computationally efficient model is proposed, based on Differential-Algebraic equations. A control scheme based on Nonlinear Model Predictive Control (NMPC) and an estimator based on Moving Horizon Estimation (MHE) is proposed to handle the wind turbulences. In order to make a real-time application of Non-linear Model Predictive Control possible, a Real-Time Iteration scheme is proposed

Comparison of linear and nonlinear model predictive control of wind turbines using LIDAR

Recent developments in remote sensing are offering a promising opportunity to rethink conventional control strategies of wind turbines. With technologies such as LIDAR, the information about the incoming wind field - the main disturbance to the system - can be made available ahead of time. Feedforward control can be easily combined with traditional collective pitch feedback controllers and has been successfully tested on real systems. Nonlinear model predictive controllers adjusting both collective pitch and generator torque can further reduce structural loads in simulations but have higher computational times compared to feedforward or linear model predictive controller. This paper compares a linear and a commercial nonlinear model predictive controller to a baseline controller. On the one hand simulations show that both controller have significant improvements if used along with the preview of the rotor effective wind speed. On the other hand the nonlinear model predictive controller can achieve better results compared to the linear model close to the rated wind speed

Model predictive controllers for reduction of mechanical fatigue in wind farms

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

Optimal control of wind turbines for distributed power generation

textWind energy represents one of the major renewable energy sources that can meet future energy demands to sustain our lifestyle. During the last few decades, the installation of wind turbines for power generation has grown rapidly worldwide. Besides utility scale wind farms, distributed wind energy systems contributes to the rise in wind energy penetration. However, the expansion of distributed wind energy systems is faced by major challenges such as the system’s reliability in addition to the environmental impacts. This work is intended to explore various control algorithms to enable the distributed wind energy systems to face the aforementioned challenges. First of all, a stall regulated fixed speed wind turbine augmented with a variable ratio gearbox has been proven to enhance the wind energy capture at a relatively low cost, and considered as an attractive design for small wind energy systems. However, the high reliability advantage of traditional fixed-speed wind turbines can be affected by the integration of the variable ratio gearbox. A portion of this work is intended to develop a control algorithm that extends the variable ratio gearbox service life, thus improves overall system reliability and reduces the expected operational cost. Secondly, a pitch regulated variable speed wind turbines dominates the wind energy industry as it represents a balance between cost and flexibility of operation. They can be used for midsized wind power generation. Optimizing its wind energy capture while maintain high system reliability has been the one of the main focuses of many researchers. Another portion of this work introduces a model predictive control framework that enhances the reliability of pitch regulated variable speed wind turbines, thus improves their operational cost. Finally, one of the major environmental challenges facing the continuous growth of wind energy industry is the noise emitted from wind turbines. The severity of the noise emission problem is more significant for small and medium sized wind turbines installed in the vicinity of residential areas for distributed power generation. Consequently, the last portion of this work is intended to investigate the potential of wind turbine control design to reduce noise emission in different operating conditions with minimal impact on power generationMechanical Engineerin

Preview-Enabled Optimal Control of Wind Turbines

Wind turbine control systems have traditionally relied on feedback measurements for rotor-speed regulation. In the mid-2000s, the use of a lidar scanner to measure the wind upstream of the turbine was demonstrated, instigating research into the use of preview disturbance measurements within the turbine controller. Over the last fifteen years, many competing feedforward control laws have been proposed and demonstrated in simulation and field tests; today, lidars are being deployed on operational wind turbines, if still largely for research purposes. In this thesis, I present a series of optimal control methods for handling preview wind speed measurements in the wind turbine blade pitch controller. This begins with disturbance-accommodating control, which seeks to directly counteract the impact of the current disturbance on the system state and can be added directly to an already-designed feedback control law. Disturbance accommodating-control is presented mainly to establish the benefits of including lidar measurements in the turbine controller. The majority of the contributions of this thesis lie in extensions to the linear-quadratic regulator to handle preview disturbance information. Theoretical developments are presented first, followed by the demonstration of the disturbance-handling linear-quadratic regulator on an experimental scaled model wind turbine operating in a wind tunnel. Finally, constraints are included in the optimal control problem, resulting in a preview-enabled linear model predictive controller. This was also tested on the scaled model turbine using a range of gusty, turbulent, and transition wind conditions. Throughout this thesis, with the exception of a small demonstration of developed theory, I employ numerical linear models of wind turbines for controller design. For nonlinear simulation-based research in this thesis and adjacent studies, I use the NREL 5MW reference turbine. The physical wind tunnel experiments were carried out on the MoWiTO 1.8 turbine, which is itself based on the properties of the NREL 5MW turbine, operated by scientists at ForWind - Center for Wind Energy Research at the University of Oldenburg.</p