A Blockchain-Empowered Cluster-Based Federated Learning Model for Blade Icing Estimation on IoT-Enabled Wind Turbine

【Author】 Cheng, Xu; Tian, Weiwei; Shi, Fan; Zhao, Meng; Chen, Shengyong; Wang, Hao

【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

【影响因子】11.648

【Abstract】Wind energy is a fast-growing renewable energy but faces blade icing. Data-driven methods provide talented solutions for blade icing detection, but a considerable amount of Internet of Things data needs to be collected to a central server, which may lead to the leakage of sensitive business data. To address this limitation, this article proposes BLADE, a Blockchain-empowered imbalanced federated learning (FL) model for blade icing detection. With the help of the Blockchain, the conventional FL is improved without worrying about the failure of the single centralized server and boosts the privacy preserving. A validation mechanism is introduced into the Blockchain to enhance the defense against poisoning attacks. In addition, a novel imbalanced learning algorithm is integrated into BLADE to solve the class imbalance problem in the sensor data. BLADE is evaluated on ten wind turbines from two wind farms. The experimental results verify the effectiveness, superiority, and feasibility of the proposed BLADE.

【Keywords】Blades; Data models; Blockchains; Wind turbines; Servers; Collaborative work; Data privacy; Blade icing detection; blockchain; federated learning (FL); imbalance learning; Internet of Things (IoT)

【发表时间】2022 DEC

【收录时间】2022-10-17

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-联邦学习