Prognostics of Thermal Anomalies in Wind Turbines via Deep Learning and Conformal Prediction Using SCADA Data

This study introduces a classification-based prognostic framework to anticipate temperature threshold exceedance events that lead to automatic shutdowns in wind turbines. The framework assesses whether an observed temperature anomaly is likely to evolve into such an event and relies exclusively on Supervisory Control and Data Acquisition data combined with Deep Learning and Conformal Prediction for uncertainty quantification. To characterize the temporal evolution of the exceedance tendency, we propose a novel metric, the Instantaneous Event Intensity, derived from confidence intervals produced by Conformal Prediction. By aggregating Instantaneous Event Intensity values over time, we define the Cumulative Event Intensity, which integrates the event magnitude. The Cumulative Event Intensity is then compared with a predefined event threshold to support binary decision-making on whether the component will experience a temperature threshold exceedance event. The proposed approach is validated on 207 wind turbines from 20 wind farms, covering three manufacturers and three distinct components, detecting 87.25% of temperature threshold exceedance events 15 days in advance, with a false positive rate below 21%. Compared with most existing prognostic studies, which are typically validated on a limited number of cases and often provide prediction horizons below two weeks, the proposed framework provides broader industrial validation and maintains early-warning capability up to three months before the event.