Cost-Sensitive Extremely Randomized Trees Algorithm for Online Fault Detection of Wind Turbine Generators

Cost-Sensitive Extremely Randomized Trees Algorithm for Online Fault Detection of Wind Turbine Generators

The number of normal samples of wind turbine generators is much larger than the number of fault samples. To solve the problem of imbalanced classification in wind turbine generator fault detection, a cost-sensitive extremely randomized trees (CS-ERT) algorithm is proposed in this paper, in which the...

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Bibliographic Details
Main Authors: Mingzhu Tang, Yutao Chen, Huawei Wu, Qi Zhao, Wen Long, Victor S. Sheng, Jiabiao Yi
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-05-01
Series:Frontiers in Energy Research
Subjects:
fault detection
fault diagnosis
cost-sensitive learning
extremely randomized trees
class imbalance
wind turbine generator
Online Access:https://www.frontiersin.org/articles/10.3389/fenrg.2021.686616/full

Internet

https://www.frontiersin.org/articles/10.3389/fenrg.2021.686616/full

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