Universität Stuttgart
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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 Detection of wind evolution and lidar trajectory optimization for lidar-assisted wind turbine control(2015) Schlipf, David; Haizmann, Florian; Cosack, Nicolai; Siebers, Tom; Cheng, Po WenIn this work a collective pitch feedforward controller for floating wind turbines is presented. The feedforward controller provides a pitch rate update to a conventional feedback controller based on a wind speed preview. The controller is designed similar to the one for onshore turbines, which has proven its capability to improve wind turbine control performance in field tests. In a first design step, perfect wind preview and a calm sea is assumed. Under these assumptions the feedforward controller is able to compensate almost perfectly the effect of changing wind speed to the rotor speed of a full nonlinear model over the entire full load region. In a second step, a nacelle-based lidar is simulated scanning the same wind field which is used also for the aero-hydro-servo-elastic simulation. With model-based wind field reconstruction methods, the rotor effective wind speed is estimated from the raw lidar data and is used in the feedforward controller after filtering out the uncorrelated frequencies. Simulation results show that even with a more realistic wind preview, the feedforward controller is able to significantly reduce rotor speed and power variations. Furthermore, structural loads on the tower, rotor shaft, and blades are decreased. A comparison to a theoretical investigation shows that the reduction in rotor speed regulation is close to the optimum.Item 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 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 WenFor 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.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.Item Open Access 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 WenRecent 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.Item Open Access 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 WenThis 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.Item Open Access Optimization of a feed-forward controller using a CW-lidar system on the CART3(2015) Haizmann, Florian; Schlipf, David; Raach, Steffen; Scholbrock, Andrew; Wright, Alan; Slinger, Chris; Medley, John; Harris, Michael; Bossanyi, Ervin; Cheng, Po WenThis work presents results from a new field-testing campaign conducted on the three-bladed Controls Advanced Research Turbine (CART3) at the National Renewable Energy Laboratory in 2014. Tests were conducted using a commercially available, nacelle-mounted continuous-wave lidar system from ZephIR Lidar for the implementation of a lidar-based collective pitch feed-forward controller. During the campaign, the data processing of the lidar system was optimized for higher availability. Furthermore, the optimal scan distance was investigated for the CART3 by means of a spectra-based analytical model and found to match the lidar's capabilities well. Throughout the campaign the predicted correlation between the lidar measurements and the turbine's reaction was confirmed from the measured data. Additionally, the baseline feedback controller's gains were tuned based on a simulation study that included the lidar system to achieve further load reductions. This led to some promising first results, which are presented at the end of this paper.Item Open Access Field testing of feedforward collective pitch control on the CART2 using a nacelle-based lidar scanner(2012) Schlipf, David; Fleming, Paul; Haizmann, Florian; Scholbrock, Andrew; Hofsäß, Martin; Wright, Alan; Cheng, Po WenThis work presents the first results from a field test to proof the concept of LIDAR assisted collective pitch control using a scanning LIDAR device installed on the nacelle of a research turbine. The purpose of the campaign was to show that a reduction of rotor speed variation is feasible with a feedforward update without changing the feedback controller. Although only a small amount of data could be collected, positive effects can be observed not only on the rotor speed but also on tower, blade and shaft loads in the case that the correlation of the wind preview and the turbine reaction is taken into account.Item Open Access 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.