SCADA-Based Long-Term Wind Turbine Performance Degradation Analysis Based on Deep Learning Techniques

Abstract

Abstract In the context of the rapid development of wind energy as a renewable resource, ensuring effective monitoring and maintenance of wind turbines is increasingly important. Despite the large volume of data collected by Supervisory Control and Data Acquisition (SCADA) systems, extracting key feature information to construct successful condition monitoring models remains a complex and challenging task. This study introduces a deep learning approach, called Convolutional Neural Network-based Support Vector Data Description (CNN-SVDD), aiming at effectively monitoring the long-term health status of wind turbines and providing timely warning for potential faults. The method employs Convolutional Neural Network (CNN) to deeply analyze time-series features and the correlations between sensors from SCADA data, capable of overcoming the challenges posed by environmental noise and changes in operational conditions. Subsequently, within the deep feature space learned by CNN, the SVDD classifier constructs a soft-boundary hypersphere. It allows for some samples to reside outside the hypersphere, thereby enhancing robustness against potential outliers in the SCADA data. Notably, the optimization processes of CNN and SVDD are linked, forming together an end-to-end process. The anomaly score generated by SVDD may act as an intelligent health indicator for assessing long-term performance degradation in wind turbines. Additionally, the radius of the hypersphere serves as a threshold for potential fault warnings. Extensive experiments on SCADA data acquired from multiple wind turbines over an 11-year period demonstrate the effectiveness of this method in early fault warning and long-term wind turbine performance degradation monitoring.