Hybrid Deep Learning for Wind Turbine Fault Detection

Wind turbine fault detection is crucial for maintaining efficient and reliable renewable energy systems. This paper introduces a novel hybrid deep learning architecture, LSTM-Attention-CapsNet, which combines Long Short-Term Memory networks, attention mechanisms, and Capsule Networks for time series-based fault detection in wind turbines. Our proposed model achieved unprecedented performance metrics when tested on a wind turbine dataset, attaining 1 accuracy, F1 score, precision, and recall. This exceptional performance marks a significant advancement in fault detection capabilities, potentially revolutionizing predictive maintenance strategies in the wind energy sector. Our findings herald a new era in wind turbine fault detection and condition monitoring, promising substantial improvements in the efficiency and reliability of wind energy production

Hybrid Deep Learning for Wind Turbine Fault Detection Keywords:

Wind Turbine , Fault Detection , Deep Learning , LSTM , Attention Mechanism , Capsule Neural Networks