A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Wind turbine blade icing detection: a federated learning approach
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. ; publishedVersion
Wind turbine blade icing detection: a federated learning approach
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. ; publishedVersion
Wind turbine blade icing detection: a federated learning approach
Cheng, Xu (author) / Shi, Fan (author) / Liu, Yongping (author) / Liu, Xiufeng (author) / Huang, Lizhen (author)
2022-01-01
cristin:2053016
10 ; 254 ; Energy
Article (Journal)
Electronic Resource
English
ICING WIND TUNNEL STUDY OF A WIND TURBINE BLADE DEICING SYSTEM
| British Library Online Contents | 2008
Preparation and anti-icing of superhydrophobic PVDF coating on a wind turbine blade
| British Library Online Contents | 2012
Aeroacoustic analysis of a wind turbine airfoil and blade on icing state condition
| American Institute of Physics | 2014
| Wiley | 2020