From Strain to Loads: Development of a Measurement Solution for Wind Turbine Transmission Input Loads during Drivetrain Testing

Author:

Bilen Eren1,Azzam Baher1ORCID,Schelenz Ralf1,Runkel Tim1,Raddatz Malte1,Jacobs Georg1ORCID

Affiliation:

1. Center for Wind Power Drives, RWTH Aachen University, 52074 Aachen, Germany

Abstract

As wind energy is paving the way for the energy transition from fossil to renewable energy sources, the ongoing trend of increasing the rated power of wind turbines aims to reduce the overall cost of wind energy. The resulting increase in drivetrain loads motivates the need for wind turbine (WT) drivetrain testing in the development phase of critical components such as the WT main gearbox (GB). While several WT system test benches allow for the application of emulated rotor loads in six degrees of freedom (6-DOF), the drivetrain input loads can significantly differ from the GB 6-DOF input loads due to the design of the drivetrain under test. However, currently available load measurement solutions are not capable of sensing GB input loads in 6-DOF. Thus, this work aims to develop a methodology for converging signals from a purposely designed sensor setup and turbine specific design parameters to compute the GB 6-DOF input loads during WT testing. Strain gauges (SG) and accelerometers have been installed on the low-speed shaft (LSS) of a WT drivetrain under test at the 4MW WT system test bench at the Center for Wind Power Drives. Using the data of the aforementioned sensors, a methodology for computing the GB input loads is developed. The methodology is validated through comparison to the applied loads data provided by the aforementioned test bench. The results demonstrate the high promise of the proposed method for estimating the GB input loads during WT drivetrain testing.

Funder

German Federal Ministry for Economic Affairs and Climate Action

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference38 articles.

1. Development of a wind turbine gearbox virtual load sensor using multibody simulation and artificial neural networks;Azzam;Forsch. Ing.,2021

2. Asgarpour, M., and Sørensen, J.D. (2015, January 7–10). State of the art in Operation and Maintenance planning of offshore wind farms. Proceedings of the European Safety & Reliability Conference (ESREL), Zurich, Switzerland.

3. Machine learning methods for wind turbine condition monitoring: A review;Stetco;Renew. Energy,2019

4. Using fuzzy linguistics to select optimum maintenance and condition monitoring strategies;Mechefske;Mech. Syst. Signal Process.,2001

5. Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges;Tchakoua;Energies,2014

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