An Ensemble Network for High-Accuracy and Long-Term Forecasting of Icing on Wind Turbines
Freezing of wind turbines causes loss of wind-generated power. Forecasting or prediction of icing on wind turbine blades based on SCADA sensor data allows taking appropriate actions before icing occurs. This paper presents a newly developed deep learning network model named PCTG (Parallel CNN-TCN GR...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/8167 |
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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 | Freezing of wind turbines causes loss of wind-generated power. Forecasting or prediction of icing on wind turbine blades based on SCADA sensor data allows taking appropriate actions before icing occurs. This paper presents a newly developed deep learning network model named PCTG (Parallel CNN-TCN GRU) for the purpose of high-accuracy and long-term prediction of icing on wind turbine blades. This model combines three networks, the CNN, TCN, and GRU, in order to incorporate both the temporal aspect of SCADA time-series data as well as the dependencies of SCADA variables. The experimentations conducted by using this model and SCADA data from three wind turbines in a wind farm have generated average prediction accuracies of about 97% for prediction horizons of up to 2 days ahead. The developed model is shown to maintain at least 95% prediction accuracy for long prediction horizons of up to 22 days ahead. Furthermore, for another wind farm SCADA dataset, it is shown that the developed PCTG model achieves over 99% icing prediction accuracy 10 days ahead. |
| format | Article |
| id | doaj-art-e03cca8da75e4234b8aa758cb1f3994b |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-e03cca8da75e4234b8aa758cb1f3994b2025-08-20T02:50:43ZengMDPI AGSensors1424-82202024-12-012424816710.3390/s24248167An Ensemble Network for High-Accuracy and Long-Term Forecasting of Icing on Wind TurbinesJiazhi 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, USAFreezing of wind turbines causes loss of wind-generated power. Forecasting or prediction of icing on wind turbine blades based on SCADA sensor data allows taking appropriate actions before icing occurs. This paper presents a newly developed deep learning network model named PCTG (Parallel CNN-TCN GRU) for the purpose of high-accuracy and long-term prediction of icing on wind turbine blades. This model combines three networks, the CNN, TCN, and GRU, in order to incorporate both the temporal aspect of SCADA time-series data as well as the dependencies of SCADA variables. The experimentations conducted by using this model and SCADA data from three wind turbines in a wind farm have generated average prediction accuracies of about 97% for prediction horizons of up to 2 days ahead. The developed model is shown to maintain at least 95% prediction accuracy for long prediction horizons of up to 22 days ahead. Furthermore, for another wind farm SCADA dataset, it is shown that the developed PCTG model achieves over 99% icing prediction accuracy 10 days ahead.https://www.mdpi.com/1424-8220/24/24/8167icing prediction on wind turbinesicing forecast using SCADA dataensemble CNN-TCN-GRU network |
| spellingShingle | Jiazhi Dai Mario Rotea Nasser Kehtarnavaz An Ensemble Network for High-Accuracy and Long-Term Forecasting of Icing on Wind Turbines Sensors icing prediction on wind turbines icing forecast using SCADA data ensemble CNN-TCN-GRU network |
| title | An Ensemble Network for High-Accuracy and Long-Term Forecasting of Icing on Wind Turbines |
| title_full | An Ensemble Network for High-Accuracy and Long-Term Forecasting of Icing on Wind Turbines |
| title_fullStr | An Ensemble Network for High-Accuracy and Long-Term Forecasting of Icing on Wind Turbines |
| title_full_unstemmed | An Ensemble Network for High-Accuracy and Long-Term Forecasting of Icing on Wind Turbines |
| title_short | An Ensemble Network for High-Accuracy and Long-Term Forecasting of Icing on Wind Turbines |
| title_sort | ensemble network for high accuracy and long term forecasting of icing on wind turbines |
| topic | icing prediction on wind turbines icing forecast using SCADA data ensemble CNN-TCN-GRU network |
| url | https://www.mdpi.com/1424-8220/24/24/8167 |
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