Estimating Health Condition of the Wind Turbine Drivetrain System

Condition Monitoring (CM) has been considered as an effective method to enhance the reliability of wind turbines and implement cost-effective maintenance. Thus, adopting an efficient approach for condition monitoring of wind turbines is desirable. This paper presents a data-driven model-based CM app...

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Bibliographic Details
Published in:Energies (Basel) Vol. 10; no. 10; p. 1583
Main Authors: Qian, Peng, Ma, Xiandong, Zhang, Dahai
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2017
Subjects:
Alternative energy sources
Bonferroni interval
condition monitoring
drivetrain
Efficiency
Energy industry
Fault diagnosis
health condition
Neural networks
online sequential extreme learning machine (OS-ELM)
Turbines
Variables
Wind power
wind turbine
United Kingdom > UK
United States > US
China
ISSN:1996-1073, 1996-1073
Online Access:Get full text
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Summary:Condition Monitoring (CM) has been considered as an effective method to enhance the reliability of wind turbines and implement cost-effective maintenance. Thus, adopting an efficient approach for condition monitoring of wind turbines is desirable. This paper presents a data-driven model-based CM approach for wind turbines based on the online sequential extreme learning machine (OS-ELM) algorithm. A physical kinetic energy correction model is employed to normalize the temperature change to the value at the rated power output to eliminate the effect of variable speed operation of the turbines. The residual signal, obtained by comparing the predicted values and practical measurements, is processed by the physical correction model and then assessed with a Bonferroni interval method for fault diagnosis. Models have been validated using supervisory control and data acquisition (SCADA) data acquired from an operational wind farm, which contains various types of temperature data of the gearbox. The results show that the proposed method can detect more efficiently both the long-term aging characteristics and the short-term faults of the gearbox.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en10101583