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Evaluation of Data Based Normal Behavior Models for Fault Detection in Wind Turbines

Wind power has been increasingly adopted as a feasible renewable power source. However, the cost maintenance of wind turbines (WT) represents a significant part of the costs in this industry sector. To alleviate that, conditioning monitoring systems are used to provide measurable data to enable anomaly detection during WT operation. In that context, the present work aims to evaluate standard machine learning methods in the task of fault predictions in WT directly from available data. More specifically, we follow a normal behavior modeling approach, where the methods learn the healthy operation of the system. We detail a methodology based on the recent literature which enables the risen of alarms in the presence of incipient faults. We perform experiments using real data from a Brazilian wind farm to evaluate 6 different models, including statistical, linear, nonlinear, static and dynamical approaches. Standard accuracy and predicted cost savings metrics indicate that our methodology is accurate enough to provide relevant savings.