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 FAST.Farm load validation for single wake situations at alpha ventus(2021) Kretschmer, Matthias; Jonkman, Jason; Pettas, Vasilis; Cheng, Po WenThe main objective of the presented work is the validation of the simulation tool FAST.Farm for the calculation of power and structural loads in single wake situations; the basis for the validation is the measurement database of the operating offshore wind farm alpha ventus. The approach is described in detail and covers the calibration of the aeroelastic turbine model, transfer of environmental conditions to simulations, and comparison between simulations and adequately filtered measurements. It is shown that FAST.Farm accurately predicts power and structural load distributions over wind direction with discrepancies of less than 10 % for most of the cases compared to the measurements. Additionally, the frequency response of the structure is investigated, and it is calculated by FAST.Farm in good agreement with the measurements. In general, the calculation of fatigue loads is improved with a wake-added turbulence model added to FAST.Farm in the course of this study.Item Open Access Surrogate modeling and aeroelastic analysis of a wind turbine with down-regulation, power boosting, and IBC capabilities(2024) Pettas, Vasilis; Cheng, Po WenAs the maturity and complexity of wind energy systems increase, the operation of wind turbines in wind farms needs to be adjustable in order to provide flexibility to the grid operators and optimize operations through wind farm control. An important aspect of this is monitoring and managing the structural reliability of the wind turbines in terms of fatigue loading. Additionally, in order to perform optimization, uncertainty analyses, condition monitoring, and other tasks, fast and accurate models of the turbine response are required. To address these challenges, we present the controller tuning and surrogate modeling for a wind turbine that is able to vary its power level in both down-regulation and power-boosting modes, as well as reducing loads with an individual blade control loop. Two methods to derive the setpoints for down-regulation are discussed and implemented. The response of the turbine, in terms of loads, power, and other metrics, for relevant operating conditions and for all control modes is captured by a data-driven surrogate model based on aeroelastic simulations following two regression approaches: a spline-based interpolation and a Gaussian process regression model. The uncertainty of the surrogate models is quantified, showing a good agreement with the simulation with a mean absolute error lower than 4% for all quantities considered. Based on the surrogate model, the aeroelastic response of the entire wind turbine for the different control modes and their combination is analyzed to shed light on the implications of the control strategies on the fatigue loading of the various components.Item Open Access On the effects of inter-farm interactions at the offshore wind farm Alpha Ventus(2021) Pettas, Vasilis; Kretschmer, Matthias; Clifton, Andrew; Cheng, Po WenItem Open Access Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements(2024) Gräfe, Moritz; Pettas, Vasilis; Dimitrov, Nikolay; Cheng, Po WenFloating offshore wind turbines (FOWTs) are equipped with various sensors that provide valuable data for turbine monitoring and control. Due to technical and operational challenges, load estimations for mooring lines and fairleads can be difficult and expensive to obtain accurately. This research delves into a methodology where simulated floater motion measurements and wind speed measurements, derived from forward-looking nacelle-based lidar, are utilized as inputs for different types of neural networks to estimate fairlead tension time series and damage equivalent loads (DELs). Fairlead tension is intrinsically linked to the dynamics and the position of the floater. Therefore, we systematically analyze the individual contribution of floater dynamics to the prediction quality of fairlead tension time series and DELs. Wind speed measurements obtained via nacelle-based lidar on floating offshore wind turbines are inherently influenced by the platform's dynamics, notably the rotational pitch displacement and surge displacement of the floater. Consequently, the lidar wind speed data indirectly contain the dynamic behavior of the floater, which, in turn, governs the fairlead loads. This study leverages lidar-measured line-of-sight (LOS) wind speeds to estimate fairlead tensions. Training data for the model are generated by the aeroelastic wind turbine simulation tool, openFAST, in conjunction with the numerical lidar simulation framework ViConDAR. The fairlead tension time series are predicted using long short-term memory (LSTM) networks. DEL predictions are made using three different approaches. First, DELs are calculated from predicted time series; second, DELs are predicted using a sequence-to-one LSTM architecture, and third, DELs are predicted using a convolutional neural network architecture. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Further, we found that lidar LOS measurements do not improve time series or DEL predictions if motion measurements are available. However, using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.