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Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a framework for the prediction of icing on wind turbines based on Supervisory Control and Data Acquisition (SCADA) data without requiring the installation of any additional icing sensors on the turbines. A Temporal Convolutional Network is considered as the model to predict icing from the SCADA data time series. All aspects of the icing prediction framework are described, including the necessary data preprocessing, the labeling of SCADA data for icing conditions, the selection of informative icing features or variables in SCADA data, and the design of a Temporal Convolutional Network as the prediction model. Two performance metrics to evaluate the prediction outcome are presented. Using SCADA data from an actual wind turbine, the model achieves an average prediction accuracy of 77.6% for future times of up to 48 h.
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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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