Full-scale wind turbine performance assessment: a customised, sensor-augmented aeroelastic modelling approach
<p>Blade erosion of wind turbines causes significant performance degradation, impairs aerodynamic efficiency, and reduces power production. However, traditional monitoring systems based on supervisory control and data acquisition (SCADA) data, which rely on operational data from turbines, lack...
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Copernicus Publications
2025-01-01
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| Series: | Wind Energy Science |
| Online Access: | https://wes.copernicus.org/articles/10/269/2025/wes-10-269-2025.pdf |
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| author | T. H. Malik C. Bak |
| author_facet | T. H. Malik C. Bak |
| author_sort | T. H. Malik |
| collection | DOAJ |
| description | <p>Blade erosion of wind turbines causes significant performance degradation, impairs aerodynamic efficiency, and reduces power production. However, traditional monitoring systems based on supervisory control and data acquisition (SCADA) data, which rely on operational data from turbines, lack effectiveness at early detection and quantification of these losses. This research builds on an established turbine performance integral (TPI) method with a sensor-augmented aeroelastic modelling approach to enhance wind turbine performance assessment, focusing on blade erosion. Applying this approach to a distinct multi-megawatt turbine model, the study integrates multibody aeroelastic simulations and real-world operational data analysis. The study identified readily available sensors that were sensitive to blade surface roughness changes caused by erosion. Operational data analysis of offshore wind turbines validated the initial sensor selection and approach. Refined simulations using further virtual sensors quantified the effect size of these sensors' output under different turbulence levels and blade states, employing Cohen's <span class="inline-formula"><i>d</i></span> – a dimensionless metric measuring the standardised difference between two means. For the turbine investigated, 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 turbine-specific, controller-informed sensor selection and emphasises the limitations of generic solutions. This research provides an approach for bridging simulation insights with operational data for turbine-specific performance assessment, contributing to the development of condition monitoring systems (CMSs), resilient turbine designs, and maintenance strategies tailored to specific operating conditions.</p> |
| format | Article |
| id | doaj-art-764aaac5e75f4815b28681f933d68186 |
| institution | Kabale University |
| issn | 2366-7443 2366-7451 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Wind Energy Science |
| spelling | doaj-art-764aaac5e75f4815b28681f933d681862025-01-24T10:43:19ZengCopernicus PublicationsWind Energy Science2366-74432366-74512025-01-011026929110.5194/wes-10-269-2025Full-scale wind turbine performance assessment: a customised, sensor-augmented aeroelastic modelling approachT. H. Malik0C. Bak1Vattenfall, Amerigo-Vespucci-Platz 2, 20457 Hamburg, GermanyDTU Wind and Energy Systems, Frederiksborgvej 399, 4000 Roskilde, Denmark<p>Blade erosion of wind turbines causes significant performance degradation, impairs aerodynamic efficiency, and reduces power production. However, traditional monitoring systems based on supervisory control and data acquisition (SCADA) data, which rely on operational data from turbines, lack effectiveness at early detection and quantification of these losses. This research builds on an established turbine performance integral (TPI) method with a sensor-augmented aeroelastic modelling approach to enhance wind turbine performance assessment, focusing on blade erosion. Applying this approach to a distinct multi-megawatt turbine model, the study integrates multibody aeroelastic simulations and real-world operational data analysis. The study identified readily available sensors that were sensitive to blade surface roughness changes caused by erosion. Operational data analysis of offshore wind turbines validated the initial sensor selection and approach. Refined simulations using further virtual sensors quantified the effect size of these sensors' output under different turbulence levels and blade states, employing Cohen's <span class="inline-formula"><i>d</i></span> – a dimensionless metric measuring the standardised difference between two means. For the turbine investigated, 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 turbine-specific, controller-informed sensor selection and emphasises the limitations of generic solutions. This research provides an approach for bridging simulation insights with operational data for turbine-specific performance assessment, contributing to the development of condition monitoring systems (CMSs), resilient turbine designs, and maintenance strategies tailored to specific operating conditions.</p>https://wes.copernicus.org/articles/10/269/2025/wes-10-269-2025.pdf |
| spellingShingle | T. H. Malik C. Bak Full-scale wind turbine performance assessment: a customised, sensor-augmented aeroelastic modelling approach Wind Energy Science |
| title | Full-scale wind turbine performance assessment: a customised, sensor-augmented aeroelastic modelling approach |
| title_full | Full-scale wind turbine performance assessment: a customised, sensor-augmented aeroelastic modelling approach |
| title_fullStr | Full-scale wind turbine performance assessment: a customised, sensor-augmented aeroelastic modelling approach |
| title_full_unstemmed | Full-scale wind turbine performance assessment: a customised, sensor-augmented aeroelastic modelling approach |
| title_short | Full-scale wind turbine performance assessment: a customised, sensor-augmented aeroelastic modelling approach |
| title_sort | full scale wind turbine performance assessment a customised sensor augmented aeroelastic modelling approach |
| url | https://wes.copernicus.org/articles/10/269/2025/wes-10-269-2025.pdf |
| work_keys_str_mv | AT thmalik fullscalewindturbineperformanceassessmentacustomisedsensoraugmentedaeroelasticmodellingapproach AT cbak fullscalewindturbineperformanceassessmentacustomisedsensoraugmentedaeroelasticmodellingapproach |