A Spatio-Temporal Multiscale Neural Network Approach for Wind Turbine Fault Diagnosis With Imbalanced SCADA Data

The supervisory control and data acquisition (SCADA) systems are widely installed on wind turbines (WTs) in major wind farms, which produce a large amount of sensory data that can be used for fault diagnosis of WTs. However, these SCADA data are naturally multivariate time series, which represent complex temporal correlations within each sensor variable and spatial correlations between different sensor variables. To effectively capture spatio-temporal correlations in SCADA data, we propose a new spatio-temporal multiscale neural network (STMNN). The proposed STMNN model contains two parallel feature extraction modules: first, a multiscale deep echo state network module to extract temporal multiscale features; second, a multiscale residual network module to extract spatial multiscale features. Additionally, as the WTs are in a normal working state most of the time, there are a large amount of normal SCADA data and few failure SCADA data. To address the data imbalance problem of SCADA data and enhance the fault diagnosis performance, instead of cross-entropy loss, the STMNN model adopts focal loss as loss function. Our proposed STMNN method can provide an end-to-end fault diagnosis solution with imbalanced SCADA data, and is evaluated through experiments on an SCADA dataset from a real wind farm. The experimental results and comparative analysis have proved the effectiveness of our proposed STMNN model in practical applications.