9
Conclusions and Recommendations
In this final chapter, the main conclusions of the research presented in this thesis are summarized in Section 9.1 and recommendations for future research are given in Section 9.2. The main contributions are recapitulated in Section 9.3.
homogeneous flow, which causes problems in situations where the wind flow differs significantly from the assumed one. In this thesis, appropriate models for ground based, floating, and nacelle based applications have been derived and used in model-based wind field reconstruction methods. In simulations and also with real data, the reconstruction methods are able to improve the average wind speed measurements compared to the conventional techniques. For control purposes, the wind field needs to be reconstructed much faster and temporal information needs to be included. Therefore, the model-based wind field reconstruction has been extended to include simple dynamic wind models, which assume that the wind field propagates with the mean wind speed according to Taylor’s Frozen Turbulence Hypothesis. With this assumption, an estimate for wind characteristics such as the rotor effective wind speed can be provided.
The most important consideration of Chapter 4 is that lidar systems measure line-of-sight wind speeds and all other information needs to be estimated. The quality of the results depends on the accuracy of the lidar and the wind model as well as on the used estimation technique.
The analytic correlation model helps to evaluate the quality of the reconstruction, to improve it, and to adjust the estimated signals for use in lidar-assisted control. With the model, the correlation of the rotor effective wind speed between a lidar system and a wind turbine can be calculated in terms of spectral quantities such as coherence and transfer functions. The model considers different rotor diameters, the spatial averaging of the individual lidar system, different scanning patterns and wind evolution, which – in combination – are responsible for the level of correlation. Very good agreement with data from a field testing campaign has shown that the model is sufficiently accurate to be usable for the following applications: First, the correlation of real or simulated lidar measurements can be compared to its expected value to identify errors in the measurement procedure or data processing. Second, the correlation model can be used to optimize the configuration or scan pattern of lidar systems, such that it provides a wind speed signal which exhibits a high correlation to the wind speed affecting the turbine and a signal which can be transferred to the control system with the preview required by the individual control approach. Third, based on the correlation model, an adaptive filter can be designed which filters out all uncorrelated frequencies for a given experimental setup. This wind field reconstruction and adaptive filter design have been combined with various lidar-assisted controllers.
9.1 Conclusions 179
9.1.2 Design and Evaluation of Lidar-Assisted Control Concepts
The controllers in this thesis have all been designed in light of their applicability on real wind turbine control systems with inputs from real lidar systems.
All controllers have been first designed independently of the measurement quality provided by a lidar system, thus assuming perfect wind preview. This approach provides the possibil-ity to distinguish between the robustness against model uncertainties and robustness against uncertainties in the provided wind preview.
Further, a reduced nonlinear model has been used for the design of all controllers. Linear controllers might provide a good control performance at a single operation point, but the dynamics and control goals of a wind turbine over the full range of wind speeds violate the linearity assumption. Additionally, simple but sufficiently accurate nonlinear models not only enable the possibilities to apply advanced nonlinear controllers, but also contribute to the fundamental understanding of the wind turbine’s behavior.
The work focuses on three lidar-assisted control concepts. All three are designed to accommo-date changes in the rotor effective wind speed and thus are combined with the adaptive filter and wind reconstruction methods described above.
The first concept is the collective pitch feedforward controller, which assists common collective pitch controllers to regulate the rotor speed in full load operations by providing an additive pitch angle command. In the case of perfect wind preview, the controller is able to almost perfectly cancel out the effect from the rotor effective wind to the rotor speed over the entire full load region and for the full aero-elastic model. This concept also indirectly reduces the impact on other states and thus decreases the structural loads of the turbine. A detailed load analysis using simulated lidar measurements and extrapolating the effects on the lifetime of a wind turbine showed promising reduction of tower, shaft, and blade loads as well as improved switching from full to partial load operations. Based on these results, field testing has been performed confirming that lidar systems are able to improve the control performance of wind turbines. Although only few data sets could be collected, the data demonstrates that it is essential to filter the data according to the correlation between the turbine and the lidar system to reduce rotor speed variation.
The second concept is the direct speed controller, which assists common generator torque controllers to track optimal inflow conditions in partial load operations. Simulations with the full aero-elastic model show that with the presented approach, the tip speed ratio can be held very close to its optimum. Although significant reduction in the deviation from the optimal tip speed ratio can be also achieved under very realistic conditions, the loads on the rotor shaft are heavily increased and the resulting energy gain is only marginal. The findings are consistent with theoretical considerations, which confirm that common torque controllers are already performing close to the aerodynamic optimum under normal inflow conditions. Finally,
the work points out that an increase of energy production in partial load operation is achievable but not attractive.
The third concept is the flatness-based feedforward controller which assists common collective pitch and generator torque controllers in the transition region between full and partial load operations. Since the reduced nonlinear wind turbine model features the system property of differential flatness, the generator torque and the collective pitch angle can be used to track trajectories of the rotor and tower motion. The trajectories are continuously designed during operation based on the wind preview. The trajectories are planned to hold the tower on its equilibrium manifold during the transition between partial and full load operations.
If perfect wind preview is assumed, the flatness-based controller outperforms the collective pitch feedforward controller because of the possibility to provide an update for the collective pitch angle and the generator torque simultaneously. The flatness-based feedforward controller exhibits sufficient robustness against model uncertainties if it is used to control the full aero-elastic model, and shows good control performance in the presence of additional measurement uncertainties. However, the tuning of the parameters is not intuitive and the baseline torque controller needs to be slightly modified.
The collective pitch feedforward controller is considered the most promising approach because of its benefits to conventional wind turbine controllers that have been confirmed in field testing and its simplicity and robustness against model and measurement uncertainties.