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Estimating Health Condition of the Wind Turbine Drivetrain System

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Estimating Health Condition of the Wind Turbine Drivetrain System. / Qian, Peng; Ma, Xiandong; Zhang, Dahai.
In: Energies, Vol. 2017, No. 10, 1583, 12.10.2017.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Qian, P, Ma, X & Zhang, D 2017, 'Estimating Health Condition of the Wind Turbine Drivetrain System', Energies, vol. 2017, no. 10, 1583. https://doi.org/10.3390/en10101583

APA

Qian, P., Ma, X., & Zhang, D. (2017). Estimating Health Condition of the Wind Turbine Drivetrain System. Energies, 2017(10), Article 1583. https://doi.org/10.3390/en10101583

Vancouver

Qian P, Ma X, Zhang D. Estimating Health Condition of the Wind Turbine Drivetrain System. Energies. 2017 Oct 12;2017(10):1583. doi: 10.3390/en10101583

Author

Qian, Peng ; Ma, Xiandong ; Zhang, Dahai. / Estimating Health Condition of the Wind Turbine Drivetrain System. In: Energies. 2017 ; Vol. 2017, No. 10.

Bibtex

@article{d6bc34cdb48d402ab4e1da8c5e42a86e,
title = "Estimating Health Condition of the Wind Turbine Drivetrain System",
abstract = "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.",
keywords = "condition monitoring, online sequential extreme learning machine (OS-ELM), Bonferroni interval, health condition, drivetrain, wind turbine",
author = "Peng Qian and Xiandong Ma and Dahai Zhang",
year = "2017",
month = oct,
day = "12",
doi = "10.3390/en10101583",
language = "English",
volume = "2017",
journal = "Energies",
issn = "1996-1073",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "10",

}

RIS

TY - JOUR

T1 - Estimating Health Condition of the Wind Turbine Drivetrain System

AU - Qian, Peng

AU - Ma, Xiandong

AU - Zhang, Dahai

PY - 2017/10/12

Y1 - 2017/10/12

N2 - 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.

AB - 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.

KW - condition monitoring

KW - online sequential extreme learning machine (OS-ELM)

KW - Bonferroni interval

KW - health condition

KW - drivetrain

KW - wind turbine

U2 - 10.3390/en10101583

DO - 10.3390/en10101583

M3 - Journal article

VL - 2017

JO - Energies

JF - Energies

SN - 1996-1073

IS - 10

M1 - 1583

ER -