Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data
Monitoring the health of wind turbine foundations is essential for ensuring their operational safety. This paper presents a cost-effective approach to obtain rotational stiffness of wind turbine foundations by using only acceleration and wind speed data that are part of SCADA data, thus lowering the...
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| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4756 |
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| author | Jiazhi Dai Mario Rotea Nasser Kehtarnavaz |
| author_facet | Jiazhi Dai Mario Rotea Nasser Kehtarnavaz |
| author_sort | Jiazhi Dai |
| collection | DOAJ |
| description | Monitoring the health of wind turbine foundations is essential for ensuring their operational safety. This paper presents a cost-effective approach to obtain rotational stiffness of wind turbine foundations by using only acceleration and wind speed data that are part of SCADA data, thus lowering the use of moment and tilt sensors that are currently being used for obtaining foundation stiffness. First, a convolutional neural network model is applied to map acceleration and wind speed data within a moving window to corresponding moment and tilt values. Rotational stiffness of the foundation is then estimated by fitting a line in the moment-tilt plane. The results obtained indicate that such a mapping model can provide stiffness values that are within 7% of ground truth stiffness values on average. Second, the developed mapping model is re-trained by using synthetic acceleration and wind speed data that are generated by an autoencoder generative AI network. The results obtained indicate that although the exact amount of stiffness drop cannot be determined, the drops themselves can be detected. This mapping model can be used not only to lower the cost associated with obtaining foundation rotational stiffness but also to sound an alarm when a foundation starts deteriorating. |
| format | Article |
| id | doaj-art-189c1a98e4dd467dbb63a0e4d87007d7 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-189c1a98e4dd467dbb63a0e4d87007d72025-08-20T03:02:51ZengMDPI AGSensors1424-82202025-08-012515475610.3390/s25154756Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA DataJiazhi Dai0Mario Rotea1Nasser Kehtarnavaz2Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USACenter for Wind Energy, University of Texas at Dallas, Richardson, TX 75080, USADepartment of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USAMonitoring the health of wind turbine foundations is essential for ensuring their operational safety. This paper presents a cost-effective approach to obtain rotational stiffness of wind turbine foundations by using only acceleration and wind speed data that are part of SCADA data, thus lowering the use of moment and tilt sensors that are currently being used for obtaining foundation stiffness. First, a convolutional neural network model is applied to map acceleration and wind speed data within a moving window to corresponding moment and tilt values. Rotational stiffness of the foundation is then estimated by fitting a line in the moment-tilt plane. The results obtained indicate that such a mapping model can provide stiffness values that are within 7% of ground truth stiffness values on average. Second, the developed mapping model is re-trained by using synthetic acceleration and wind speed data that are generated by an autoencoder generative AI network. The results obtained indicate that although the exact amount of stiffness drop cannot be determined, the drops themselves can be detected. This mapping model can be used not only to lower the cost associated with obtaining foundation rotational stiffness but also to sound an alarm when a foundation starts deteriorating.https://www.mdpi.com/1424-8220/25/15/4756monitoring of wind turbine foundationrotational stiffness of wind turbine foundationdetection of wind turbine foundation degradation |
| spellingShingle | Jiazhi Dai Mario Rotea Nasser Kehtarnavaz Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data Sensors monitoring of wind turbine foundation rotational stiffness of wind turbine foundation detection of wind turbine foundation degradation |
| title | Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data |
| title_full | Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data |
| title_fullStr | Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data |
| title_full_unstemmed | Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data |
| title_short | Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data |
| title_sort | obtaining rotational stiffness of wind turbine foundation from acceleration and wind speed scada data |
| topic | monitoring of wind turbine foundation rotational stiffness of wind turbine foundation detection of wind turbine foundation degradation |
| url | https://www.mdpi.com/1424-8220/25/15/4756 |
| work_keys_str_mv | AT jiazhidai obtainingrotationalstiffnessofwindturbinefoundationfromaccelerationandwindspeedscadadata AT mariorotea obtainingrotationalstiffnessofwindturbinefoundationfromaccelerationandwindspeedscadadata AT nasserkehtarnavaz obtainingrotationalstiffnessofwindturbinefoundationfromaccelerationandwindspeedscadadata |