Icing detection and prediction for wind turbines using multivariate sensor data and machine learning
Feng Ye, Ahmed Aziz Ezzat
Abstract
Adverse weather events can significantly compromise the availability and economics of a wind farm. This paper focuses on rotor icing detection, which constitutes a major challenge in wind farm operation. When ice accumulates on wind turbine blades, it causes substantial generation losses, operational disruptions, and safety hazards to the personnel, assets, and equipment in a wind farm. Alerts about early signs of rotor icing can assist operators in proactively initiating icing mitigation measures. To this end we propose a machine-learning-based framework that effectively learns the unique signatures of icing events. The framework effectively extracts salient features by condensing the multivariate turbine sensor data into a small-sized subset of information-rich descriptors. Those, along with power-curve-derived features, are used to train a deep-learning-based model that flags icing events and estimates icing probabilities. We also propose a new loss measure, called the icing power loss error (IPLE), that realistically quantifies the expected icing-related power losses. Our experiments show that the proposed framework achieves up to 96.4% accuracy in flagging icing events, while keeping the number of false alarms at minimum. When compared to prevalent data-driven benchmarks, up to 18.7% reduction in power loss estimation error is realized.