Fakultät Maschinenbau
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Wind turbines play a crucial role in the revolution towards renewable resources. They need to be economically competitive to be sustainable. This still requires to lower the cost of energy (COE). To this end, nonlinear model predictive control (NMPC) is used within this paper. As known from literature, NMPC significantly improves the energy extracting performance as well as the mitigation of tower loads of wind turbines. As it is shown in this paper, the drivetrain fatigue strength drops disproportionally in parallel, which either increases the demands on the turbine design or decreases the lifetime of drivetrain components. Without additional boundary conditions to the underlying optimal control problems (OCPs), the application of energy-maximizing NMPC might so even raise the COE. Only penalizing axial torque oscillations by quadratic terms decreases energy-extracting performance below the level of classical wind turbine controllers. This makes more sophisticated conditions necessary. In this paper, the increase of the drivetrain damage by NMPC is analyzed, and appropriate boundary conditions are derived, to balance the two contradicting objectives of energy maximization and drivetrain load mitigation. An NMPC approach based on indirect methods is used, to obtain a solution of the OCPs very efficiently. It applies the Hamilton equations and Pontryagin's maximum principle. Its accuracy and efficiency to solve OCPs was presented over the last decades.
Maintenance strategies of drivetrains either focus on cases of failure or act preventively. The case of failure of a single element within a drivetrain can lead to the failure of the whole machinery or even an entire production line. A maintenance strategy, which focuses only on the case of failure, holds therefore economic risks. A preventive maintenance strategy can lead to replacements of intact components, because one strictly relies on fix maintenance intervals. The useful life of these replaced components is not fully utilized. A further development is the combination of one or two maintenance strategies mentioned above with condition monitoring (CM) in addition. CM monitors crucial components of the machinery. If CM indicates first signs of maloperation, unusual signals or amplitudes, one can initiate maintenance. The significance of a CM relies on the amount, placement and positioning of sensors. In reality, placement and positioning is restricted by design space and operation conditions. Current Research at HSA aims on the development of a predictive maintenance strategy, which combines CM with a realtime-capable digital twin. The latter bases on a multibody system and can be considered as a virtual CM. Within this, the CM provides boundary conditions and realtime validation for the digital twin. The virtual CM suffers no restrictions such as design space and operation conditions. It is able to predict the behavior of the drivetrain at any time, at any place, in any resolution. The data provided by the virtual CM can be used to reevaluate maximum utilization of components and therefore it can be used to set up a predictive maintenance strategy. To investigate the possibilities of this predicitve maintenance strategy further, a drivetrain test bench has been developed at HSA from scratch. The main component is a modular, three-stage gearbox which allows different setups of parameters in which e.g. center distance, bearing concepts and transmission ratio can be changed and modified. Several high fidelity sensors provide CM in realtime. The Paper will focus on the comparison of real and virtual signals and shows first results of validations in frequency domain.