Wind Turbine Condition Monitoring Based on SCADA Data–Image Conversion

This article investigates a data-image conversion-based condition monitoring algorithm for wind turbines (WTs) using supervisory control and data acquisition (SCADA) data. The traditional condition monitoring problem is converted into an image classification problem in the proposed method. It consists of three parts, feature selection, data-image conversion, and condition monitoring based on image classification. The important features are selected from SCADA data by gradient boosting decision tree (GBDT) and permutation importance. Through the data-image conversion method, the selected numerical features are converted into heatmap images and combined into RGB images. AlexNet is introduced to classify the generated images to detect the operation state of WT. Data augmentation, inspired by symbolic augmentation, is developed to expand the number of fault data images to solve the overfitting caused by uneven data during training. The effectiveness of the proposed condition monitoring method is validated on the dataset collected from Chinese wind farms. The comparison result shows the proposed image-based condition monitoring method has achieved significant improvements.