Preprints
https://doi.org/10.5194/wes-2024-49
https://doi.org/10.5194/wes-2024-49
29 Apr 2024
 | 29 Apr 2024
Status: this preprint is currently under review for the journal WES.

Full Scale Wind Turbine Performance Assessment: A Customised, Sensor-Augmented Aeroelastic Modelling Approach

Tahir H. Malik and Christian Bak

Abstract. Blade erosion on wind turbines causes significant performance degradation, impairing aerodynamic efficiency and reducing power production. However, traditional SCADA based monitoring systems lack effectiveness for early detection and quantification of these losses. This research builds on an established method with a sensor-augmented aeroelastic modelling approach to enhance wind turbine performance assessment, focusing on blade erosion. Applying this approach to a distinct turbine model, the study integrates HAWC2 aeroelastic simulations with real-world operational data analysis. Preliminary simulations identified readily available sensors sensitive to blade surface roughness changes caused by erosion. Operational data analysis validated the initial sensor selection and the method. Refined simulations with various virtual sensors were conducted, utilising Cohen's d to quantify the effect size of sensor readings across different turbulence levels and blade states. Findings indicate that sensors such as blade tip torsion, blade root flap moment, shaft moment and tower moments, especially under lower turbulence intensities, are particularly sensitive to erosion. This confirms the need for a turbine-specific, controller-informed approach to sensor selection and highlights the limitations of generic solutions. This research offers a framework for bridging simulation insights with operational data, enabling the enhancement of condition monitoring systems (CMS), resilient turbine designs and maintenance strategies tailored to operating conditions.

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Tahir H. Malik and Christian Bak

Status: open (until 08 Jun 2024)

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Tahir H. Malik and Christian Bak
Tahir H. Malik and Christian Bak

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Short summary
This research integrates custom sensors into wind turbine simulation models for improved performance monitoring utilising a developed method. Real-world data validation demonstrates that enhanced sensor accuracy increases annual energy production and extends operational lifespan. This approach addresses the need for precise performance assessments in the evolving wind energy sector, ultimately promoting sustainability and efficiency.
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