Early anomaly detection of wind turbine gearbox based on SLFormer neural network

Abstract

Detecting faults in wind turbines at early stage is of great significance in improving the economic efficiency of wind farms. However, the widely used fault detection techniques are mostly based on traditional deep learning frameworks, which are limited in handling long-term dependencies, leading to constraints in global feature extraction. Additionally, current studies often build models based on the operational performance of an individual wind turbine, limiting the generalizability of the models. Therefore, this study introduces an improved model called SLFormer, which integrates Long Short-Term Memory into the Transformer encoder for early fault detection in the gearbox. Simultaneously, a novel fault detection strategy based on SCADA data analysis and transfer learning is proposed. Case studies from three wind farms indicate that the SLFormer model significantly outperforms six other popular prediction models in terms of stability and accuracy in modeling normal behavior and exhibits high robustness to random disturbances in SCADA data. The SLFormer model can predict gearbox anomalies twenty-one days in advance and effectively avoids false fault reports. The proposed strategy can help the model acquire fault knowledge from multiple wind farms, thus creating a framework with generalizability.