Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data

• URL: http://arxiv.org/abs/2503.08325v1
• Date: Tue, 11 Mar 2025 11:37:43 GMT
• Title: Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data
• Authors: Lele Qi, Mengna Liu, Xu Cheng, Fan Shi, Xiufeng Liu, Shengyong Chen,
• Abstract summary: We propose a prototype learning model for class-imbalanced data in heterogeneous environments to detect wind turbine blade icing.<n>Experiments on real data from 20 turbines across two wind farms show our method outperforms five FL models and five class imbalance methods.
• Score: 23.229445493233573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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