Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
Monitoring the structural health of wind turbine blades is essential to increase energy capture and operational safety of turbines, and therewith enhance competitiveness of wind energy. With the current trends of designing blades ever longer, detailed knowledge of the vibrational characteristics at...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/9/4294 |
| _version_ | 1827693011076644864 |
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| author | Theresa Loss Alexander Bergmann |
| author_facet | Theresa Loss Alexander Bergmann |
| author_sort | Theresa Loss |
| collection | DOAJ |
| description | Monitoring the structural health of wind turbine blades is essential to increase energy capture and operational safety of turbines, and therewith enhance competitiveness of wind energy. With the current trends of designing blades ever longer, detailed knowledge of the vibrational characteristics at any point along the blade is desirable. In our approach, we monitor vibrations during operation of the turbine by wirelessly measuring accelerations on the outside of the blades. We propose an algorithm to extract so-called vibration-based fingerprints from those measurements, i.e., dominant vibrations such as eigenfrequencies and narrow-band noise. These fingerprints can then be used for subsequent analysis and visualisation, e.g., for comparing fingerprints across several sensor positions and for identifying vibrations as global or local properties. In this study, data were collected by sensors on two test turbines and fingerprints were successfully extracted for vibrations with both low and high operational variability. An analysis of sensors on the same blade indicates that fingerprints deviate for positions at large radial distance or at different blade sides and, hence, an evaluation with larger datasets of sensors at different positions is promising. In addition, the results show that distributed measurements on the blades are needed to gain a detailed understanding of blade vibrations and thereby reduce loads, increase energy harvesting and improve future blade design. In doing so, our method provides a tool for analysing vibrations with relation to environmental and operational variability in a comprehensive manner. |
| first_indexed | 2024-03-10T11:34:54Z |
| format | Article |
| id | doaj.art-b483d23cca3240d686ba650469660b7b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:34:54Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b483d23cca3240d686ba650469660b7b2023-11-21T18:58:38ZengMDPI AGApplied Sciences2076-34172021-05-01119429410.3390/app11094294Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine BladesTheresa Loss0Alexander Bergmann1Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, Inffeldgasse 33/I, 8010 Graz, AustriaInstitute of Electrical Measurement and Sensor Systems, Graz University of Technology, Inffeldgasse 33/I, 8010 Graz, AustriaMonitoring the structural health of wind turbine blades is essential to increase energy capture and operational safety of turbines, and therewith enhance competitiveness of wind energy. With the current trends of designing blades ever longer, detailed knowledge of the vibrational characteristics at any point along the blade is desirable. In our approach, we monitor vibrations during operation of the turbine by wirelessly measuring accelerations on the outside of the blades. We propose an algorithm to extract so-called vibration-based fingerprints from those measurements, i.e., dominant vibrations such as eigenfrequencies and narrow-band noise. These fingerprints can then be used for subsequent analysis and visualisation, e.g., for comparing fingerprints across several sensor positions and for identifying vibrations as global or local properties. In this study, data were collected by sensors on two test turbines and fingerprints were successfully extracted for vibrations with both low and high operational variability. An analysis of sensors on the same blade indicates that fingerprints deviate for positions at large radial distance or at different blade sides and, hence, an evaluation with larger datasets of sensors at different positions is promising. In addition, the results show that distributed measurements on the blades are needed to gain a detailed understanding of blade vibrations and thereby reduce loads, increase energy harvesting and improve future blade design. In doing so, our method provides a tool for analysing vibrations with relation to environmental and operational variability in a comprehensive manner.https://www.mdpi.com/2076-3417/11/9/4294vibration monitoringstructural healthwind turbineswireless sensors |
| spellingShingle | Theresa Loss Alexander Bergmann Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades Applied Sciences vibration monitoring structural health wind turbines wireless sensors |
| title | Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades |
| title_full | Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades |
| title_fullStr | Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades |
| title_full_unstemmed | Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades |
| title_short | Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades |
| title_sort | vibration based fingerprint algorithm for structural health monitoring of wind turbine blades |
| topic | vibration monitoring structural health wind turbines wireless sensors |
| url | https://www.mdpi.com/2076-3417/11/9/4294 |
| work_keys_str_mv | AT theresaloss vibrationbasedfingerprintalgorithmforstructuralhealthmonitoringofwindturbineblades AT alexanderbergmann vibrationbasedfingerprintalgorithmforstructuralhealthmonitoringofwindturbineblades |