06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7
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Item Open Access Comparison of feedforward and model predictive control of wind turbines using LIDAR(2012) Schlipf, David; Pao, Lucy Y.; Cheng, Po WenLIDAR systems are able to provide preview information of wind disturbances at various distances in front of wind turbines. This technology paves the way for new control concepts such as feedforward control and model predictive control. This paper compares a nonlinear model predictive controller and a feedforward controller to a baseline controller. Realistic wind "measurements" are obtained using a detailed simulation of a LIDAR system. A full lifetime comparison shows the advantages of using the wind predictions to reduce wind turbine fatigue loads on the tower and blades as well as to limit the blade pitch rates. The results illustrate that the feedforward controller can be combined with a tower feedback controller to yield similar load reductions as the model predictive controller.Item Open Access An approach for automated detection and classification of pavement cracks(2017) Al-Mistarehi, Bara'; Schwieger, Volker (Prof. Dr.-Ing. habil.)Item Open Access Development of a model predictive controller for floating offshore wind turbines(2020) Nann, SamuelIn this work, an Economic Model Predictive Controller for a floating offshore wind turbine is presented. The classical Model Predictive Control for floating offshore wind turbines provides promising results. In addition, research on onshore wind turbines revealed the potential of the economic control method, which can improve the closed-loop behavior and simplify the control design in comparison to the classical version of this control method. The aim of this work is, to develop a novel Economic Model Predictive Controller for a floating offshore wind turbine based on these two research results. A simplified low order model of a floating offshore wind turbine serves as a basis for the controller design. Including the disturbance preview and constraints, the controller computes optimal trajectories for the blade pitch and the generator torque. To apply the control technique to a floating offshore wind turbine two things have to be done: Firstly, the cost function is designed, to fulfill the main objectives of, maximizing the generated power and alleviating the structural fatigues. Secondly, the constraints are integrated into the control problem. After selecting a suitable solver, the controller is discretized and scaled, thus a proper implementation and smooth operation is possible. Afterwards, the successful functioning of the algorithm, a multi-objective optimization is done, to find appropriate weights to adjust the cost function for the required objectives. Finally, the developed controller is tested with realistic wind and wave disturbances. A significant reduction of the standard deviation of the generated power can be shown, while maintaining real time capability. Furthermore, the structural fatigues of the tower and the platform are decreased.Item Open Access Berechnung und Bewertung der Schallausbreitung von Windenergieanlagen mit Hilfe von Messdaten(2023) Häfele, Lorenz KarlItem Open Access Nonlinear model predictive control of floating wind turbines(2013) Schlipf, David; Sandner, Frank; Raach, Steffen; Matha, Denis; Cheng, Po WenIn this work a nonlinear model predictive control method for a floating wind turbine is presented. A reduced nonlinear model including disturbance preview of wind and waves is derived and implemented to compute optimal input trajectories for collective pitch and the generator torque. A cost functional is introduced which fulfills all desired constraints and controller goals for above rated wind conditions. The controller is tested for extreme and fatigue load cases and a significant reduction of the power and rotor speed deviations is obtained. Furthermore, ultimate tower loads and damage equivalent loads on shaft and blades are decreased. Although more detailed testing is necessary, this preliminary results show the advantages of nonlinear model predictive control for floating wind turbines.Item Open Access Nonlinear model predictive control of floating wind turbines with individual pitch control(2014) Raach, Steffen; Schlipf, David; Sandner, Frank; Matha, Denis; Cheng, Po WenIn this work a nonlinear model predictive controller with individual pitch control for a floating offshore wind turbine is presented. An aerodynamic model of the collective pitch control approach is extended by describing pitching and yawing moments based on rotor disk theory. This extension is implemented in a reduced nonlinear model of the floating wind turbine including disturbance preview of wind speed, linear vertical and horizontal wind shear, and wave height to compute optimal input trajectories for the individual pitch control inputs and the generator torque. An extended cost functional for individual pitch control is proposed based on the collective pitch control approach. The controller is evaluated in aero-servo-hydro-elastic simulations of a 5MW reference wind turbine disturbed by a three-dimensional stochastic turbulent wind field. Results show a significant blade fatigue load reduction compared to a baseline controller through minimizing yawing and pitching moments on the rotor hub while maintaining the advantages of the model predictive control approach with collective pitch control.Item Open Access Monitoring of the production process of graded concrete component using terrestrial laser scanning(2021) Yang, Yihui; Balangé, Laura; Gericke, Oliver; Schmeer, Daniel; Zhang, Li; Sobek, Werner; Schwieger, VolkerItem Open Access Load analysis of look-ahead collective pitch control using LIDAR(2010) Schlipf, David; Fischer, Tim; Carcangiu, Carlo Enrico; Rossetti, Michele; Bossanyi, ErvinIn a detailed analysis the benefit of LIDAR assisted collective pitch control is evaluated, by using a realistic LIDAR simulator and comparing it to an advanced feedback controller. With the proposed look-ahead controller best load reduction can be observed for high turbulence and high wind speed. Damage equivalent loads on tower and blades are reduced up to 20% and 10%, respectively.Item Open Access Optimization of the groundwater remediation process using a coupled genetic algorithm-finite difference method(2021) Seyedpour, Seyed Morteza; Valizadeh, Iman; Kirmizakis, Panagiotis; Doherty, Rory; Ricken, TimIn situ chemical oxidation using permanganate as an oxidant is a remediation technique often used to treat contaminated groundwater. In this paper, groundwater flow with a full hydraulic conductivity tensor and remediation process through in situ chemical oxidation are simulated. The numerical approach was verified with a physical sandbox experiment and analytical solution for 2D advection-diffusion with a first-order decay rate constant. The numerical results were in good agreement with the results of physical sandbox model and the analytical solution. The developed model was applied to two different studies, using multi-objective genetic algorithm to optimise remediation design. In order to reach the optimised design, three objectives considering three constraints were defined. The time to reach the desired concentration and remediation cost regarding the number of required oxidant sources in the optimised design was less than any arbitrary design.Item Open Access LIDAR assisted collective pitch control(2011) Schlipf, David; Bossanyi, Ervin; Carcangiu, Carlo Enrico; Fischer, Tim; Maul, Timo; Rossetti, MicheleNacelle based pulsed LIDAR (Light detection and ranging) systems provide preview information of wind disturbances at various distances in front of wind turbines. In previous work it has been shown that this information can be used to improve the speed regulation of wind turbines by a look-ahead update to the collective pitch control, which indicates load reduction of tower and blades. In the scope of the UpWind project a first fatigue and extreme load analysis has been done to concretize the improvement of look-ahead collective pitch control using LIDAR.