Active Trailing Edge Flap System fault detection via Machine Learning

Abstract. Active trailing edge flap systems (AFlap) have shown promising results in reducing wind turbine (WT) loads. Once the WT design includes the AFlap, a condition monitoring system will be needed to ensure the flaps provide the expected load reductions. This paper presents two approaches based on machine learning to diagnose the health state of an AFlap system. Both approaches rely only on the sensors commonly available on commercial WTs, avoiding the need and the cost of additional measurement systems. The first approach (MFS) uses manual feature engineering in combination with a random forest classifier. The second approach (AFS) relies on random convolutional kernels to create the feature vectors. The study shows that the MFS method is reliable in classifying all the investigated combinations of AFlap health states in the case of asymmetrical flap faults not only when the WT operates in normal power production but also before startup. Instead, the AFS method can identify some of the AFlap health states for both asymmetrical and symmetrical faults when the WT is in normal power production.