Dynamic Fault Diagnosis in Wind Turbines: A GNN-LSTM Approach

Wind turbines are pivotal in renewable energy production, yet their efficient operation is hindered by potential faults and failures. Traditional fault diagnosis methods often struggle to adapt to the dynamic nature of these systems, leading to delayed or inaccurate detection of faults. To overcome this challenge, research proposed aninnovative approach leveraging Graph Neural Networks (GNNs) and Long Short-Term Memory (LSTM) networks for dynamic fault diagnosis in wind turbines. Previous methods typically rely on static models or simplistic time-series analysis, limiting their ability to capture the complex interactions within wind turbine systems. The approach integrates GNNs to model the spatial relationships between various components of the turbine, enabling a more holistic understanding of the system's behavior. Additionally, LSTM networks are employed to capture temporal patterns and dynamics, allowing for real-time fault detection and prediction. Through extensive trials on real-world wind turbine data, research establish the effectiveness of proposed GNN-LSTM approach. The method outperforms traditional static models and time-series analysis techniques in terms of fault detection accuracy and timeliness. By accurately identifying faults as they occur and predicting potential failures, this approach enables proactive maintenance strategies, minimizing downtime and maximizing turbine efficiency. In conclusion, the integration of GNNs and LSTM networks offers a promising avenue for dynamic fault diagnosis in wind turbines. By leveraging both spatial and temporal information, the approach significantly improves the reliability and efficiency of wind turbine operations, contributing to the advancement of renewable energy technologies. The accuracy of 95.2% achieved by this proposed GNN-LSTM approach in dynamic fault diagnosis for wind turbines underscores its efficacy in accurately detecting and predicting faults within these complex systems was implemented in python.