Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data
Abstract
1. Introduction
2. Dataset and Data Preprocessing
3. Mapping Model
3.1. Architecture of CNN Model
- (i)
- Input Layer—Accepts a sequence of 2-channel time-series/frequency map data over a window of size T.
- (ii)
- First Convolutional Block—A 1D convolution layer with 32 filters of a kernel size 3. Then, ReLU (Rectified Linear Unit) activation is applied, followed by a max pooling layer that reduces the temporal dimension by a factor of 2
- (iii)
- Second Convolutional Block—The feature map output of the first convolution block is passed through a second convolution layer with 64 filters (also with a kernel size 3, ReLU activation), followed by another max pooling layer
- (iv)
- Flatten and Dense Layers—The pooled features are flattened into a 1D vector and passed through a fully connected layer with 128 units and ReLU activation
- (v)
- Dropout Layer—To prevent overfitting, a dropout layer with a rate of 0.2 is added.
- (vi)
- Output Layer—Finally, a dense layer with linear activation is used to output a 2D vector representing the estimated tilt and moment
3.2. Window Size Selection
4. Synthetic Data Generation
Architecture of Autoencoder Network
5. Results and Discussion
5.1. Results of Estimating Stiffness by the Mapping Model
5.2. Results of Detecting a Drop in Stiffness
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Turbine Number | Ground Truth Stiffness (GN·m/rads) | Number of Data Samples | R2 |
---|---|---|---|
24 | 73.1 | 7200 | 0.95 |
37 | 66.5 | 2620 | 0.96 |
40 | 50.6 | 6610 | 0.81 |
44 | 41.2 | 6010 | 0.82 |
46 | 74.7 | 5780 | 0.93 |
Window Size (Samples) | Stiffness Estimation Absolute Error (GN·m/Rads) |
---|---|
10 | 4.5 |
20 | 2.7 |
30 | 1.3 |
40 | 4.2 |
Turbine Number | Ground Truth Stiffness (GN·m/rads) | Estimated Stiffness (GN·m/rads) Mean ± Std | t-Distribution 95% Confidence Interval |
---|---|---|---|
24 | 73.1 | 74.4 ± 3.9 | [73.2, 75.4] |
37 | 66.5 | 69.9 ± 6.8 | [67.9, 71.8] |
40 | 50.6 | 53.2 ± 6.9 | [51.3, 55.2] |
44 | 41.2 | 44.1 ± 3.4 | [43.2, 45.1] |
46 | 74.7 | 77.2 ± 3.3 | [76.3, 78.2] |
Turbine Number | Ground Truth Stiffness (GN·m/rads) | Estimated Stiffness (Moment Only) (GN·m/rads) Mean ± Std | t-Distribution 95% Confidence Interval |
---|---|---|---|
24 | 73.1 | 73.4 ± 2 | [72.8, 74.0] |
37 | 66.5 | 66.5 ± 2.3 | [65.9, 67.1] |
40 | 50.6 | 51.4 ± 3.7 | [50.3, 52.4] |
44 | 41.2 | 43.4 ± 3.0 | [42.6, 44.3] |
46 | 74.7 | 77.1 ± 1.4 | [76.7, 77.6] |
Turbine 24 | Artificial drop in stiffness | −1 | −2 | −3 | −4 | −5 |
Measured stiffness | Estimated stiffness | 72.6 | 72.8 | 72.5 | 72.5 | 72.0 |
73.1 | Detected drop in stiffness | −0.1 | −0.7 | −1.0 | −1.4 | −1.6 |
Turbine 37 | Artificial drop in stiffness | −1 | −2 | −3 | −4 | −5 |
Measured stiffness | Estimated stiffness | 67.6 | 67.2 | 67.7 | 67.6 | 68.2 |
66.5 | Detected drop in stiffness | −0.9 | −1.3 | −2.3 | −3.0 | −4.8 |
Turbine 40 | Artificial drop in stiffness | −1 | −2 | −3 | −4 | −5 |
Measured stiffness | Estimated stiffness | 50.7 | 50.3 | 50.9 | 50.6 | 50.0 |
50.6 | Detected drop in stiffness | −0.1 | −0.2 | −0.9 | −1.3 | −1.6 |
Turbine 44 | Artificial drop in stiffness | −1 | −2 | −3 | −4 | −5 |
Measured stiffness | Estimated stiffness | 41.2 | 41.3 | 41.3 | 41.9 | 41.6 |
41.2 | Detected drop in stiffness | −0.9 | −1.6 | −2.2 | −3.6 | −4.1 |
Turbine 46 | Artificial drop in stiffness | −1 | −2 | −3 | −4 | −5 |
Measured stiffness | Estimated stiffness | 74.8 | 74.8 | 74.5 | 74.3 | 74.3 |
74.7 | Detected drop in stiffness | −0.4 | −0.7 | −0.8 | −1.3 | −1.6 |
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Dai, J.; Rotea, M.; Kehtarnavaz, N. Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data. Sensors 2025, 25, 4756. https://doi.org/10.3390/s25154756
Dai J, Rotea M, Kehtarnavaz N. Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data. Sensors. 2025; 25(15):4756. https://doi.org/10.3390/s25154756
Chicago/Turabian StyleDai, Jiazhi, Mario Rotea, and Nasser Kehtarnavaz. 2025. "Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data" Sensors 25, no. 15: 4756. https://doi.org/10.3390/s25154756
APA StyleDai, J., Rotea, M., & Kehtarnavaz, N. (2025). Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data. Sensors, 25(15), 4756. https://doi.org/10.3390/s25154756