Wind farms, typically in high-latitude regions, face a high risk of blade icing. Traditional centralized training methods raise serious privacy concerns. To enhance data privacy in detecting wind turbine blade icing, traditional federated learning (FL) is employed. However, data heterogeneity, resulting from collections across wind farms in varying environmental conditions, impacts the model's optimization capabilities. Moreover, imbalances in wind turbine data lead to models that tend to favor recognizing majority classes, thus neglecting critical icing anomalies. To tackle these challenges, we propose a federated prototype learning model for class-imbalanced data in heterogeneous environments to detect wind turbine blade icing. We also propose a contrastive supervised loss function to address the class imbalance problem. Experiments on real data from 20 turbines across two wind farms show our method outperforms five FL models and five class imbalance methods, with an average improvement of 19.64\% in \( mF_{\beta} \) and 5.73\% in \( m \)BA compared to the second-best method, BiFL.

本研究解决了在高纬度地区风力发电场中，因环境条件差异导致的数据异构性和类不平衡问题，从而影响风力涡轮机叶片结冰检测的效果。提出了一种联邦原型学习模型，并引入对比监督损失函数，显著提升了检测性能，实验证明相较于其他方法平均提升了19.64%的mFβ和5.73%的mBA。这一贡献增强了数据隐私保护，并有效识别了关键的结冰异常。

基于原型的异构联邦学习用于风力涡轮机叶片结冰检测