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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.
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This content will become publicly available on June 16, 2026
Multivariate Time-Series Modelling for Wind Turbine Subsystem Reliability Prediction
Abstract Wind turbine reliability monitoring and prediction are crucial for early failure detection, proactive maintenance, performance optimization, and cost reduction, especially as many utility-scale turbines near the midpoint or end of their operational lifespan. We proposed a wind turbine subsystem reliability prediction model to facilitate week-ahead forecasts of the occurrence, duration, and type of potential failures or probability of downtime for major turbine components, including the blade, hub, gearbox, generator, inverter, electrical, and control subsystems. Specifically, we developed an Instance-Normalization Decomposition Linear (IN-DLinear) model, grounded in deep time-series modelling theory. The distribution shifts in turbine state features across the training, validation, and test datasets, as well as across various time scales, were effectively mitigated with IN. The long-term inertia in turbine state features was addressed by decomposing the input time-series data to effectively capture seasonality. The efficacy of IN-DLinear is systematically evaluated using 10-year field measurements from a 2.5-MW wind turbine. IN-DLinear exhibited superior performance, reducing prediction errors by 13%∼30% compared to mean value judgment and other deep time-series models, including Seq2Seq, Transformer, and Autoformer.
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- Award ID(s):
- 2443363
- PAR ID:
- 10645630
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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