Measuring dynamic mechanical torque with fiber-optic sensors for geared wind turbines
U. Gutierrez Santiago (TU Delft - Team Jan-Willem van Wingerden)
J.W. Wingerden – Promotor (TU Delft - Team Jan-Willem van Wingerden)
H. Polinder – Promotor (TU Delft - Transport Engineering and Logistics)
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Abstract
The devastating effects of climate change are becoming more evident each day. We are running out of time, and our best collective effort is needed to revert the situation and ensure a sustainable and healthy future. Renewable energies are a key enabler for such a future. Wind energy has attained remarkable progress in the last decades, and the growth required to meet future net-zero scenarios is extraordinary. Wind turbine technology is rapidly evolving towards larger rotors and power ratings, resulting in much higher torque density demands for the drivetrain in general and the gearbox in particular. Maintaining or even improving gearbox reliability with increasing torque density demands is proving to be challenging. Accurate knowledge of the mechanical loads of wind turbine gearboxes has become essential for modern highly loaded gearbox designs with significant dynamic interactions. The contribution of this dissertation can be summarized by its goal: to develop a method to measure dynamic mechanical torque in geared wind turbines. A key requirement was set to enable fleet-wide implementation to monitor torque throughout the complete service life of the wind turbines. This dissertation proposes a method based on strain measurements on the outer surface of the static first-stage ring gear that overcomes the main drawback of traditional methods. A series of experiments are presented, ranging from proof-of-concept tests conducted using gearbox test benches to an extensive field validation campaign. These experiments have advanced the technology readiness level and demonstrated the accuracy and robustness of the proposed method, which is now deemed ready for commercial implementation. Future research should explore improving drivetrain loading using dynamic mechanical torque measurements with novel data-driven control strategies. Additionally, recursively tracking operational deflection shapes over time is recommended for fault detection.