Online health assessment and fault prediction for wind turbine generator

A health assessment and fault prediction method for wind turbine generators is proposed in this article. In health assessment module, considering generator status transferring along with environment and wind turbine–self operating, variables under wind turbine normal working are divided into two par...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering Journal of systems and control engineering, 2022-04, Vol.236 (4), p.718-730
Main Authors: Wang, Junda, Zhang, Jing, Jiang, Na, Song, Na, Xin, Jinghao, Li, Ning
Format: Article
Language:English
Subjects:
Deterioration
Fault diagnosis
Mechanical engineering
Modules
Parameters
Probabilistic models
Support vector machines
Turbines
Turbogenerators
Wind power
Wind turbines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A health assessment and fault prediction method for wind turbine generators is proposed in this article. In health assessment module, considering generator status transferring along with environment and wind turbine–self operating, variables under wind turbine normal working are divided into two parameter spaces and recognized, namely operating conditions and status parameters. Then generator health benchmark models based on Gaussian mixture model are established in different operating condition sub-spaces after the data imbalance problem solved. For online health assessment, health deterioration index based on condition recognition models is calculated and a dual-threshold alarm scheme is proposed. When an alarm is raised by degraded health deterioration index, the program could access fault prediction module, where the generator rear bearing temperature trend and fault remaining time can be predicted through weights redistribution and hyper-parameter optimized support vector regression. In experiments, the proposed health assessment and fault prediction was verified in a real wind farm, and results showed this method could assess generator condition accurately and improve special fault prediction performance.
ISSN:0959-6518
2041-3041
DOI:10.1177/09596518211056165