15 Fakultätsübergreifend / Sonstige Einrichtung

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/16

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Author: search.filters.author.Schlipf, DavidSubject: search.filters.subject.620

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Now showing 1 - 5 of 5
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    Design and evaluation of a lidar-based feedforward controller for the INNWIND.EU 10 MW wind turbine
    (2015) Fürst, Holger; Schlipf, David; Iribas Latour, Mikel; Cheng, Po Wen
    For the development of the next generation of multi megawatt wind turbines, advanced control concepts are one of the major tasks. Reduction of fatigue and extreme loading could help to improve the overall design process and make plants more cost effective. This work deals with the application of the promising methodology of feedforward control using nacelle-based lidar sensor measurements on a 10 MW wind turbine concept. After lidar data processing has been described, the feedforward controller is designed such that disturbances from the changing wind speed to the generator speed are compensated by adding an update to the collective pitch rate signal of the normal feedback controller. The evaluation of the feedforward controller is done in two steps: Firstly, simulations using perfect lidar data measurements are applied to check the robustness of the controller against model uncertainties. After that, simulations with realistic lidar measurements are investigated. To improve control performance, the scanning configuration of the used lidar system is optimized. Over all it can be shown that lidar-assisted control leads to significant load reductions, especially in the full load region of the 10 MW turbine.
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    Comparison of linear and nonlinear model predictive control of wind turbines using LIDAR
    (2014) Schlipf, David; Grau, Patrick; Raach, Steffen; Duraiski, Ricardo; Trierweiler, Jorge; Cheng, Po Wen
    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.
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    An adaptive data processing technique for lidar-assisted control to bridge the gap between lidar systems and wind turbines
    (2015) Schlipf, David; Fleming, Paul; Raach, Steffen; Scholbrock, Andrew; Haizmann, Florian; Krishnamurthy, Raghu; Boquet, Matthieu; Cheng, Po Wen
    This paper presents first steps toward an adaptive lidar data processing technique crucial for lidar-assisted control in wind turbines. The prediction time and the quality of the wind preview from lidar measurements depend on several factors and are not constant. If the data processing is not continually adjusted, the benefit of lidar-assisted control cannot be fully exploited or can even result in harmful control action. An online analysis of the lidar and turbine data is necessary to continually reassess the prediction time and lidar data quality. In this work, a structured process to develop an analysis tool for the prediction time and a new hardware setup for lidar-assisted control are presented. The tool consists of an online estimation of the rotor effective wind speed from lidar and turbine data and the implementation of an online cross-correlation to determine the time shift between both signals. Further, we present initial results from an ongoing campaign in which this system was employed for providing lidar preview for feedforward pitch control.
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    Modeling and identification of nonlinear systems using MIMO LEM-Hammerstein structure
    (2006) Schlipf, David; Bolognese Fernandes, Pedro; Trierweiler, Jorge O.
    This paper extends the LEM-Hammerstein models already presented in the literature to MIMO systems. Instead of linear time-invariant subsystems in association with static nonlinearities, LEM-Hammerstein and LEM-Wiener systems exhibit nonlinear static features and operating-point dependent dynamics, and can therefore model a broader class of system than the conventional block-oriented models. In order to avoid the problem of solving the partial differential equations necessary for the construction of the steady-state mapping that appears in the model, a modified controller normal form is proposed, and the model is constructed on the basis of an extended, non-minimal state-space realization. Moreover, the identification strategy already used with LEM systems can be applied in order to construct such models from experimental data, and the techniques destined for analysis and control of Hammerstein systems can be applied promptly. An application of these concepts to the modeling and identification is demonstrated in the numerical example of a level system constituted by six connected tanks.
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    Modeling and identification of nonlinear systems using SISO LEM-Hammerstein and LEM-Wiener model structures
    (2006) Bolognese Fernandes, Pedro; Schlipf, David; Trierweiler, Jorge O.
    This paper applies the concept of linearization around the equilibrium manifold (LEM) already presented in the literature in order to construct model structures that can be viewed as extensions of the conventional Wiener and Hammerstein models. Instead of linear time-invariant subsystems in association with static nonlinearities, these extensions exhibit variable dynamic character and can therefore model a broader class of systems than the conventional cited approaches. Moreover, the identification strategy already used with LEM systems can be applied in order to construct such models from experiments, and the techniques destined for analysis and control of Wiener and Hammerstein systems can be applied promptly. To application of these concepts to the modeling and identification is demonstrated with a numerical example, considering a heat exchange system.