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

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    IEA Wind Task 32: Wind Lidar : identifying and mitigating barriers to the adoption of wind lidar
    (2018) Clifton, Andrew; Clive, Peter; Gottschall, Julia; Schlipf, David; Simley, Eric; Simmons, Luke; Stein, Detlef; Trabucchi, Davide; Vasiljevic, Nikola; Würth, Ines
    IEA Wind Task 32 exists to identify and mitigate barriers to the adoption of lidar for wind energy applications. It leverages ongoing international research and development activities in academia and industry to investigate site assessment, power performance testing, controls and loads, and complex flows. Since its initiation in 2011, Task 32 has been responsible for several recommended practices and expert reports that have contributed to the adoption of ground-based, nacelle-based, and floating lidar by the wind industry. Future challenges include the development of lidar uncertainty models, best practices for data management, and developing community-based tools for data analysis, planning of lidar measurements and lidar configuration. This paper describes the barriers that Task 32 identified to the deployment of wind lidar in each of these application areas, and the steps that have been taken to confirm or mitigate the barriers. Task 32 will continue to be a meeting point for the international wind lidar community until at least 2020 and welcomes old and new participants.
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    Optimization of floating offshore wind turbine platforms with a self-tuning controller
    (2017) Lemmer, Frank; Müller, Kolja; Yu, Wei; Schlipf, David; Cheng, Po Wen
    The dynamic response of floating offshore wind turbines is complex and requires numerous design iterations in order to converge at a cost-efficient hull shape with reduced responses to wind and waves. In this article, a framework is presented, which allows the optimization of design parameters with respect to user-defined criteria such as load reduction and material costs. The optimization uses a simplified nonlinear model of the floating wind turbine and a self-tuning model-based controller. The results are shown for a concrete three-column semi-submersible and a 10MW wind turbine, for which a reduction of the fluctuating wind and wave loads is possible through the optimization. However, this happens at increased material costs for the platform due to voluminous heave plates or increased column spacing.
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    Flatness-based feedforward control of wind turbines using Lidar
    (2014) Schlipf, David; Cheng, Po Wen
    Current lidar technology is offering a promising opportunity to take a fresh look at wind turbine control. This work evaluates a flatness-based feedforward approach, that allows to calculate the control action based on trajectories of the rotor speed and tower motion using wind measurements. The trajectories are planned online considering actuator constrains to regulate the rotor speed and minimize tower movements. The feedforward signals of the collective pitch and generator torque update can be combined with conventional feedback controllers. This facilitates the application on commercial wind turbines. Simulations using a realistic lidar simulator and a full aero-elastic model show considerable reduction of tower and shaft loads.
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    Realistic simulations of extreme load cases with lidar-based feedforward control
    (2017) Hagemann, Tim; Haizmann, Florian; Schlipf, David; Cheng, Po Wen
    This work presents the development of a simulation environment which allows to simulate realistic extreme events with lidar-based feedforward control. This environment includes turbulent wind fields including extreme events, wind evolution and wind field scanning with a nacelle-based lidar system. It is designed to simulate lidar-based controllers in a realistic environment. In addition, a controller extension is proposed to identify and mitigate extreme events in wind fields based on lidar measurements. The combination of this extreme event controller with the realistic simulation environment is a promising tool for load reductions in wind turbines.
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    Lidar-based wake tracking for closed-loop wind farm control
    (2017) Raach, Steffen; Schlipf, David; Cheng, Po Wen
    This work presents two advancements towards closed-loop wake redirection of a wind turbine. First, a model-based wake-tracking approach is presented, which uses a nacelle-based lidar system facing downwind to obtain information about the wake. The method uses a reduced-order wake model to track the wake. The wake tracking is demonstrated with lidar measurement data from an offshore campaign and with simulated lidar data from a simulation with the Simulator fOr Wind Farm Applications (SOWFA). Second, a controller for closed-loop wake steering is presented. It uses the wake-tracking information to set the yaw actuator of the wind turbine to redirect the wake to a desired position. Altogether, the two approaches enable a closed-loop wake redirection.
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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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    Statistical load estimation using a nacelle-based lidar system
    (2010) Bischoff, Oliver; Hofsäß, Martin; Rettenmeier, Andreas; Schlipf, David; Siegmeier, Björn
    The paper presents the results of statistical load analyses based on data measured at the 5MW AREVA Wind M5000 onshore prototype. Measurements with standard meteorological measurement devices are analysed and compared to measurements with a pulsed LIDAR system which is enhanced with a multi-purpose scanning device installed on the top of the nacelle of the turbine. Based on these measurements statistical summaries of relevant meteorological parameters have been used for normative procedures to calculate the mechanical loads which occur at the wind energy turbine. It could be verified that LIDAR systems can substitute standard measurement devices for a load estimation of wind energy turbines.
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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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    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 Wen
    This 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.