Wind turbine blade icing risk assessment considering power output predictions based on SCSO-IFCM clustering algorithm

Author:

Wang LeiORCID,He Yigang,He YinglongORCID,Zhou Yazhong,Zhao QingwuORCID

Funder

Wuhan University

Publisher

Elsevier BV

Reference44 articles.

1. An efficacious model for predicting icing-induced energy loss for wind turbines;Swenson;Appl. Energy,2022

2. Comprehensive analysis of the impact of the icing of wind turbine blades on power loss in cold regions;Chuang;J. Mar. Sci. Eng.,2023

3. Investigation of ice accretion effect on the aerodynamic characteristics of a wind turbine blade tip after a short icing event;Kangash,2023

4. Evaluation of health status of wind turbine based on multiple evidence method;Hu;Acta Energiae Solaris Sin.,2018

5. Health assessment methods for wind turbines based on power prediction and mahalanobis distance;Zhan;Int. J. Pattern Recogn. Artif. Intell.,2019

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