Iterative feedback tuning of feedforward IPC for two-bladed wind turbines: A comparison with conventional IPC

The development of sustainable energy production methods is an important aspect in lowering the emission of greenhouse gases and exhaustion of fossil fuels. Wind energy is recognized globally as one of the most promising sustainable forms of electricity generation, but the cost of offshore wind energy does not meet the level of fossil-based energy sources. An opportunity for wind energy cost reduction is the deployment of two-bladed wind turbines at offshore locations. Two-bladed turbines save the mass and cost of one rotor blade, which allows the entire wind turbine construction to be designed lighter, and in effect leads to lower initial costs. Moreover, reduction of harmonic fatigue loads on blades and other turbine parts using Individual Pitch Control (IPC) is a way to extend the turbine lifetime. This type of control, using a feedback control structure incorporating the Multi-Blade Coordinate (MBC) transformation, is capable of mitigating the most dominant periodic loads. It is generally known that significant turbine load reductions can be achieved using IPC, however, it is unclear to what extent the MBC pitch signal is optimal in terms of load alleviations. The main goal of this thesis is to develop a self-learning feedforward IPC strategy for a state-ofthe- art two-bladed wind turbine. This IPC strategy will be compared to the conventional feedback IPC implementation. By making use of properties of the MBC transformation, implementations of yaw control by IPC in different configurations are evaluated in terms of performance and stability. As a preparation for the comparison between conventional feedback and self-learning feedforward IPC strategies, a linear control-oriented model from blade pitch angles to harmonic blade loads is identified and used throughout this work for two main purposes. The first purpose is to reveal the level of interaction between both blades, which turns out to be negligible. On the basis of this reasoning, an appropriate cost-function is implemented for optimization of the feedforward controller. The second purpose of the linear model is the simplified and faster development of the Iterative Feedback Tuning (IFT) algorithm, which is later implemented in high-fidelity non-linear wind turbine simulation software. IFT is a self-learning model-free algorithm, and is used to optimize the rotor-position dependent feedforward IPC implementation. It is shown that the self-learning algorithm succeeds in optimization of the feedforward controller at all constant wind speeds, but also in more realistic turbulent wind conditions in the above-rated region. As the feedforward controller generates a constant amplitude pitch signal for each wind speed, the amplitude of the feedforward pitch signal is gain-scheduled on exogenous load signals, in an effort to improve feedforward performance. Results show that the conventional IPC strategy is optimal in terms of load reductions in steady state wind conditions, as the IFT algorithm optimizes to the exact same pitch signal at various constant wind speeds. In turbulent wind conditions, performance results indicate that the constant amplitude feedforward controller is able to attain significant load reductions, but that the performance of the conventional feedback control method is still superior. Comparing the pitch signals of both controllers in turbulent wind conditions, reveals that the conventional method continuously changes the phase of the implemented pitch signal, which is not driven by the varying rotor speed. To see how these changing pitch periodics have an effect on the load reduction capabilities, the feedforward (rotor speed dependent) pitch signal amplitude is scheduled on exogenous signals in various ways. This scheduling shows only minor performance improvements, and it can be concluded that the frequency changes in the pitch signal imposed by MBC, help to actively mitigate periodic blade loads. Using both the azimuth and blade load measurements, the conventional IPC strategy seems to actively track and mitigate the current present blade load harmonic, and it appears to be a serious challenge to develop control strategies that can improve performance already attained by MBC.