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This Ph.D. thesis focuses on the numerical methods, the exemplary applications, and the verification of a recently developed high-fidelity simulation framework. This framework builds a digital copy of multiple ground-fixed or floating scaled wind turbines in a boundary layer wind tunnel. Various aspects of the numerical approach are considered, with the goals to reduce the need for tuning, improve accuracy, ensure numerical stability and limit computational cost., Diese Dissertation konzentriert sich auf die Entwicklung, die numerischen Methoden, die Verifikation und beispielhafte Anwendungen eines High-Fidelity-Simulations-Frameworks. Das Framework stellt eine digitale Kopie fixierter oder schwimmender Windkraftanlagen in einem Grenzschichtwindkanal dar. Verschiedene Aspekte des numerischen Ansatzes werden betrachtet, mit den Zielen Tuning zu reduzieren, Genauigkeit zu verbessern, numerische Stabilität sicherzustellen und Rechenkosten zu begrenzen.

This paper studies a closed-loop wind farm control framework for active power control (APC) with a simultaneous reduction of wake-induced structural loads within a fully developed wind farm flow interacting with the atmospheric boundary layer. The main focus is on a classical feedback control, which features a simple control architecture and a practical measurement system that are realizable for real-time control of large wind farms. We demonstrate that the wake-induced structural loading of the downstream turbines can be alleviated, while the wind farm power production follows a reference signal. A closed-loop APC is designed first to improve the power-tracking performance against wake-induced power losses of the downwind turbines. Then, the nonunique solution of APC for the wind farm is exploited for aggregated structural load alleviation. The axial induction factors of the individual wind turbines are considered control inputs to limit the power production of the wind farm or to switch to greedy control when the demand exceeds the power available in the wind. Furthermore, the APC solution domain is enlarged by an adjustment of the power set-points according to the locally available power at the waked wind turbines. Therefore, the controllability of the wind turbines is improved for rejecting the intensified load fluctuations inside the wake. A large-eddy simulation model is employed for resolving the turbulent flow, the wake structures, and its interaction with the atmospheric boundary layer. The applicability and key features of the controller are discussed with a wind farm example consisting of 3×4 turbines with different wake interactions for each row. The performance of the proposed APC is evaluated using the accuracy of the wind farm power tracking and the wake-induced damage equivalent fatigue loads of the towers of the individual wind turbines.

Measurement campaigns in wind energy research are becoming increasingly complex, which has exacerbated the difficulty of taking optimal measurements using light detection and ranging (LiDAR) systems. Compromises between spatial and temporal resolutions are always necessary in the study of heterogeneous flows, like wind turbine wakes. Below, we develop a method for space-time conversion that acts as a temporal fluid-dynamic interpolation without incurring the immense computing costs of a 4D flow solver. We tested this space-time conversion with synthetic LiDAR data extracted from a large-eddy-simulation (LES) of a neutrally stable single-turbine wake field. The data was synthesised with a numerical LiDAR simulator. Then, we performed a parametric study of 11 different scanning velocities. We found that temporal error dominates the mapping error at low scanning speeds and that spatial error becomes dominant at fast scanning speeds. Our space-time conversion method increases the temporal resolution of the LiDAR data by a factor 2.4 to 40 to correct the scan-containing temporal shift and to synchronise the scan with the time code of the LES data. The mean-value error of the test case is reduced to a minimum relative error of 0.13% and the standard-deviation error is reduced to a minimum of 0.6% when the optimal scanning velocity is used. When working with the original unprocessed LiDAR measurements, the space-time-conversion yielded a maximal error reduction of 69% in the mean value and 58% in the standard deviation with the parameters identified with our analysis.

We compare the spectra of local generalized eigenvalue problems in different adaptive coarse spaces for overlapping and nonoverlapping domain decomposition methods. In particular, we compare the AGDSW (Adaptive Generalized Dryja-Smith-Widlund), the OS-ACMS (Overlapping Schwarz-Approximate Component Mode Synthesis), and the SHEM (Spectral Harmonically Enriched Multiscale) coarse spaces for overlapping Schwarz methods, the GenEO (Generalized Eigenproblems in the Overlaps) coarse space for FETI-1 and BDD methods, and two approaches based on estimates for the $P_D$ operator for FETI-DP and BDDC methods. Therefore, we consider eight different two-dimensional coefficient functions with jumps ranging from simple channels to a realistic microstructure of a dual-phase steel. We observe significant differences in the width of the gap between good and bad eigenvalues depending on the coefficient distribution. In addition to that, eigenvalue problems involving sophisticated but more expensive harmonic extensions or deluxe-scaling can reduce the number of bad eigenvalues.

Adaptive coarse spaces for domain decomposition methods are an active area of research to make iterative domain decomposition methods robust with respect to large discontinuities in the material parameters or almost incompressible elasticity. In order to make their use feasible for applications, the computational overhead of the adaptive methods has to be reduced while maintaining the robustness. Since the solution of the local eigenvalue problems is expensive, also the iterative eigenvalue solvers are considered in detail and potential improvements are presented.

Wind measurements can reduce the uncertainty in the prediction of wind energy production. Today, commercially available scanning lidars can scan the atmosphere up to several kilometres. Here, we use lidar measurements to forecast near-coastal winds with lead times of 5 min. Using Taylor’s frozen turbulence hypothesis together with local topographic corrections, we demonstrate that wind speeds at a downstream position can be forecast by using measurements from a scanning lidar performed upstream in a very short-term horizon. The study covers 10 periods characterised by neutral and stable atmospheric conditions. Our methodology shows smaller forecasting errors than those of the persistence method and the autoregressive integrated moving average (ARIMA) model. We discuss the applicability of this forecasting technique with regards to the characteristics of the lidar trajectories, the site-specific conditions and the atmospheric stability.

The prospects of active wake deflection control to mitigate wake-induced power losses in wind farms have been demonstrated by large eddy simulations, wind tunnel experiments, and recent field tests. However, it has not yet been fully understood how the yaw control of wind farms should take into account the variability in current environmental conditions in the field and the uncertainty in their measurements. This research investigated the influence of dynamic wind direction changes on active wake deflection by intended yaw misalignment. For this purpose the wake model FLORIS was used together with wind direction measurements recorded at an onshore meteorological mast in flat terrain. The analysis showed that active wake deflection has a high sensitivity towards short-term wind directional changes. This can lead to an increased yaw activity of the turbines. Fluctuations and uncertainties can cause the attempt to increase the power output to fail. Therefore a methodology to optimize the yaw control algorithm for active wake deflection was introduced, which considers dynamic wind direction changes and inaccuracies in the determination of the wind direction. The evaluation based on real wind direction time series confirmed that the robust control algorithm can be tailored to specific meteorological and wind farm conditions and that it can indeed achieve an overall power increase in realistic inflow conditions. Furthermore recommendations for the implementation are given which could combine the robust behaviour with reduced yaw activity.

Very short-term forecasts of wind power provide electricity market participants with extremely valuable information, especially in power systems with high penetration of wind energy. In very short-term horizons, statistical methods based on historical data are frequently used. This paper explores the use of dual-Doppler radar observations of wind speed and direction to derive five-minute ahead deterministic and probabilistic forecasts of wind power. An advection-based technique is introduced, which estimates the predictive densities of wind speed at the target wind turbine. In a case study, the proposed methodology is used to forecast the power generated by seven turbines in the North Sea with a temporal resolution of one minute. The radar-based forecast outperforms the persistence and climatology benchmarks in terms of overall forecasting skill. Results indicate that when a large spatial coverage of the inflow of the wind turbine is available, the proposed methodology is also able to generate reliable density forecasts. Future perspectives on the application of Doppler radar observations for very short-term wind power forecasting are discussed in this paper.

For short-term power predictions and estimations of the available power during curtailment of a wind farm, it is necessary to consider the flow dynamics and aerodynamic interactions of the turbines. In this paper, a control-oriented dynamic two-dimensional wind farm model is introduced that aims to incorporate real-time measurements such as flow velocities at turbine locations to estimate the ambient wind farm flow. The model is intended to derive flow predictions for real-time applications. Since fully resolved computational fluid dynamics are too CPU-intensive for such a task, the dynamic model presented in this paper relies on an approximation of the flow equations in a two-dimensional framework. A semi-Lagrangian advection scheme and a step-wise flow solver together offer fast calculation speed, which scales linearly with the number of grid points. In order to emulate effects of realistic three-dimensional wind farm flow, a relaxation of the two-dimensional continuity equation is presented. Furthermore, with little extra computational expense, additional dynamic state variables for various possible applications can be propagated along the wind flow. For instance, a dynamic confidence parameter can provide estimations of the accuracy of flow predictions, while a turbulence parameter adds the possibility to estimate wake induced loads on downstream turbines. In order to demonstrate the performance and validity of the new model it is compared with other models. At first a two turbine reference case is compared with a steady-state model and secondly with results obtained by the dynamic wind farm flow model WFSim. Finally a small wind farm is simulated in order to show the computational scaling of the model., Team Jan-Willem van Wingerden