Wind Turbine Fault Identification in Sample Imbalance Scenarios Using FRBCS With GAN Oversampling and Metric Learning

Wind Turbine Fault Identification in Sample Imbalance Scenarios Using FRBCS With GAN Oversampling and Metric Learning

The operation data from different wind turbines (WTs) is not universal, due to the influence of installation, location, and environmental conditions, which caused incomplete and imbalanced fault sample data, and influenced the fault identification accuracy. Therefore, this paper proposes a fuzzy rul...

Full description

Saved in:
Bibliographic Details
Main Authors: Changsheng Kang, Xiaoyi Qian, Lixin Wang, Ziheng Dai, Shuai Guan, Yi Zhao, Wenyao Sun
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Fault identification
fuzzy rule-based classification system
generative adversarial network
imbalanced dataset
metric learning
wind turbine
Online Access:https://ieeexplore.ieee.org/document/10815943/
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items