Preprints
https://doi.org/10.5194/wes-2026-133
https://doi.org/10.5194/wes-2026-133
30 Jul 2026
 | 30 Jul 2026
Status: this preprint is currently under review for the journal WES.

Low-Frequency Fatigue monitoring using nacelle-mounted accelerometers

Mustapha Chaar, Wout Weijtjens, and Christof Devriendt

Abstract. Thrust loading on wind turbines causes quasi-static loading that ultimately introduces fatigue loading on the turbine. Monitoring these quasi-static loads is relevant for assessing the residual lifetime of the asset. However, installing strain gauges on all assets is practically and economically infeasible; only a subset of turbines, referred to as fleet leaders, are instrumented to this level. Interestingly, these quasi-static loads also result in a temporary inclination or tilt of the tower, which can be picked up by accelerometers or inclinometers installed on the tower or nacelle. This paper proposes using gravity-sensitive tri-axial MEMS accelerometers placed in the nacelle to estimate the inclination and load characteristics of a fleet of turbines. To do this accurately, the MEMS sensor needs to be calibrated to offset installation errors and compensate for the turbines’ permanent inclination. For this purpose, long-term data from the MEMS accelerometer are processed, and the constituting offsets are estimated. After calibration, the quasi-static angles induced by loading can be estimated from the accelerometer. The derived load characteristics can then be used to extrapolate bending moments from fully instrumented fleet-leader turbines to the remainder of the wind farm. The bending moment is then used to assess damage-equivalent moments, and it was found that the accelerometer produces damage estimates similar to those obtained from strain gauges. Thus, the approach enables scalable and cost-effective farmwide load monitoring.

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Mustapha Chaar, Wout Weijtjens, and Christof Devriendt

Status: open (until 27 Aug 2026)

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Mustapha Chaar, Wout Weijtjens, and Christof Devriendt
Mustapha Chaar, Wout Weijtjens, and Christof Devriendt
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Short summary
With advances in sensor technology, wind farm operators are installing low-cost sensors across the fleet to monitor structural integrity. However, these sensors cannot directly measure fatigue or remaining lifetime. This study shows that they can provide this information by learning from their peers with more instrumentation. The results demonstrate that slow-changing loads linked to high fatigue can be estimated accurately which are key to extending turbine lifetime.
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