Wind turbine blade icing detection: a federated learning approach

• Published in: Energy (Oxford) 2022-09, Vol.254, p.124441, Article 124441
• Main Authors: Cheng, Xu, Shi, Fan, Liu, Yongping, Liu, Xiufeng, Huang, Lizhen
• Format: Article
• Language: English
• Subjects: Blade icing detection; data collection; energy; Federated learning; humans; Imbalanced learning; information management; ownership; risk; Time series classification; wind; Wind turbine; wind turbines
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2022.124441

Wind farms are often located at high latitudes, which entails a high risk of icing for wind turbine blades. Traditional anti-icing methods rely primarily on manual observation, the use of special materials, or external sensors/tools, but these methods are limited by human experience, additional costs, and understanding of the mechanical mechanism. Model-based approaches rely heavily on prior knowledge and are subject to misinterpretation. Data-driven approaches can deliver promising solutions but require large datasets for training, which might face significant challenges with respect to data management, e.g., privacy protection and ownership. To address these issues, this paper proposes a federated learning (FL) based model for blade icing detection. The proposed approach first creates a prototype-based model for each client and then aggregates all client models into a globally weighted model. The clients use a prototype-based modeling method to address the data imbalance problem, while using the FL-based learning method to ensure data security and safety. The proposed model is comprehensively evaluated using data from two wind farms, with 70 wind turbines. The results validate the effectiveness of the proposed prototype-based client model for feature extraction, and the superiority over the five baselines in terms of icing detection accuracy. In addition, the experiment demonstrates the promising result of online blade icing detection, with almost 100% accuracy. •A federated learning framework for wind turbine blade icing detection.•Prototype learning method for addressing imbalance data problems.•A distributed learning approach for addressing data management related issues.•An online learning framework for anomaly detection.