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Experimental validation of virtual torque sensing for wind turbine gearboxes based on strain measurements
  • +4
  • Jelle Bosmans,
  • Simone Gallas,
  • Victor Smeets,
  • Matteo Kirchner,
  • Luk Geens,
  • Jan Croes,
  • Wim Desmet
Jelle Bosmans
Katholieke Universiteit Leuven Departement Werktuigkunde

Corresponding Author:[email protected]

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Simone Gallas
Katholieke Universiteit Leuven Departement Werktuigkunde
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Victor Smeets
Katholieke Universiteit Leuven Departement Werktuigkunde
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Matteo Kirchner
Katholieke Universiteit Leuven Departement Werktuigkunde
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Luk Geens
ZF Wind Power Antwerpen NV
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Jan Croes
Forcebit BV
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Wim Desmet
Katholieke Universiteit Leuven Departement Werktuigkunde
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Abstract

In efforts to reduce the operation and maintenance cost of wind turbines, there is an increasing interest to monitor key turbine quantities such as the torque load on the gearbox. Monitoring the torque paves the way for the calculation of remaining useful lifetime, leading to cost reductions through improved reliability and maintenance planning. In order to avoid expensive direct torque sensors, this paper investigates the potential of virtual torque sensing, a technique based on 3 basic components: firstly, a set of non-intrusive sensors installed on the gearbox. Three groups of strain gauges on the gearbox as well an angular encoder are considered in this paper. Secondly, a physics-based model, capable of predicting the response of aforementioned sensors. These models are constructed with a purposeful balance between accuracy and computational cost. Model validation and updating are performed to ensure efficient and accurate prediction of the sensor output. Finally, an Augmented Extended Kalman Filter (AEKF) is used to combine the measured response with predictions from the model to infer the gearbox input torque. Since a key factor determining the performance of the AEKF is the tuning of the AEKF covariance matrices, multiple methods are introduced to systematically tune the covariance matrices. Experimental validation results show that the virtual torque sensor can detect the load torque with a Normalized Mean Absolute Error (NMAE) between 3 .41% to 7 .47% , depending on the sensor set. The influence of the amount of sensors used and the tuning method are also investigated.