With the rapid development of the power industry, the monitoring of the state of power generation units has garnered widespread attention. In order to effectively detect whether wind turbine blades are iced, this paper proposes a wind turbine blade icing detection method based on SCADA (Supervisory Control and Data Acquisition) image data enhancement. The method initially reconstructs features based on the mechanism of icing, considering both data relevance and feature importance for feature selection. Subsequently, SCADA data is transformed into a two-dimensional image format to meet the input requirements of two-dimensional neural network models. On this basis, an optimized CycleGAN algorithm is used to generate more adaptable image data, aiming to address the issue of data category imbalance and significantly enhance the generalization ability of the model. This study selected WT15 data as the training set and WT21 data as the test set. The experimental results demonstrate that the feature selection method proposed in this paper leads to an increase of 4.79 percentage points in model accuracy and 2.64 percentage points in F1 score, compared to feature selection using the XGBoost model. In comparison to the original CycleGAN model, the improved model achieved an increase in accuracy of 6.78 percentage points and an improvement in the F1 score of 6.91 percentage points. These findings indicate that the method proposed in this paper offers a significant advantage in improving the model's accuracy and generalisation ability.