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A condition monitoring system for an early warning of developing faults in wind turbine electrical systems

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A condition monitoring system for an early warning of developing faults in wind turbine electrical systems. / Ma, Xiandong; Cross, Philip; Qian, Peng.

In: Insight – Non-Destructive Testing and Condition Monitoring, Vol. 58, No. 12, 01.12.2016, p. 663-670.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Ma, X, Cross, P & Qian, P 2016, 'A condition monitoring system for an early warning of developing faults in wind turbine electrical systems', Insight – Non-Destructive Testing and Condition Monitoring, vol. 58, no. 12, pp. 663-670. https://doi.org/10.1784/insi.2016.58.12.663

APA

Ma, X., Cross, P., & Qian, P. (2016). A condition monitoring system for an early warning of developing faults in wind turbine electrical systems. Insight – Non-Destructive Testing and Condition Monitoring, 58(12), 663-670. https://doi.org/10.1784/insi.2016.58.12.663

Vancouver

Ma X, Cross P, Qian P. A condition monitoring system for an early warning of developing faults in wind turbine electrical systems. Insight – Non-Destructive Testing and Condition Monitoring. 2016 Dec 1;58(12):663-670. https://doi.org/10.1784/insi.2016.58.12.663

Author

Ma, Xiandong ; Cross, Philip ; Qian, Peng. / A condition monitoring system for an early warning of developing faults in wind turbine electrical systems. In: Insight – Non-Destructive Testing and Condition Monitoring. 2016 ; Vol. 58, No. 12. pp. 663-670.

Bibtex

@article{40a1550a16ed441d822268d9f211d832,
title = "A condition monitoring system for an early warning of developing faults in wind turbine electrical systems",
abstract = "Electrical condition monitoring (CM) normally involves the collection of high-frequency, instantaneous data for feature extraction. This paper presents a novel development of an electrical condition monitoring system for wind turbines. The system is developed based upon a control and data acquisition system, for which hardware modules can be configured for a particular set of signals, thus tailoring the system to a specific range of monitoring tasks. A wavelet-based singularity detection method is proposed, which automatically calculates the Lipschitz exponent, a measure to describe the local transient activities in the measurement signal. The relationship between the Lipschitz exponent and the type and severity of faults occurring on the grid and in the power electronics is explored. The proposed algorithms are tested and validated using simulation data from computer simulations of a doubly-fed induction generator (DFIG) wind turbine with a grid connection. A field-programmable gate array (FPGA) embedded in the system has been utilised, allowing the signal processing tasks to be undertaken in real-time for monitoring purposes. The paper demonstrates that a fault signal of small magnitude generated at the early stage of a fault carries the same Lipschitz exponent as the signal of large magnitude generated at the late stage of the fault, thereby providing an early warning before the fault develops into a detrimental one.",
keywords = "Reliability, Condition monitoring, Operation and maintenance, Real-time simulation, Wind turbines",
author = "Xiandong Ma and Philip Cross and Peng Qian",
year = "2016",
month = dec,
day = "1",
doi = "10.1784/insi.2016.58.12.663",
language = "English",
volume = "58",
pages = "663--670",
journal = "Insight – Non-Destructive Testing and Condition Monitoring",
issn = "1354-2575",
publisher = "British Institute of Non-Destructive Testing",
number = "12",

}

RIS

TY - JOUR

T1 - A condition monitoring system for an early warning of developing faults in wind turbine electrical systems

AU - Ma, Xiandong

AU - Cross, Philip

AU - Qian, Peng

PY - 2016/12/1

Y1 - 2016/12/1

N2 - Electrical condition monitoring (CM) normally involves the collection of high-frequency, instantaneous data for feature extraction. This paper presents a novel development of an electrical condition monitoring system for wind turbines. The system is developed based upon a control and data acquisition system, for which hardware modules can be configured for a particular set of signals, thus tailoring the system to a specific range of monitoring tasks. A wavelet-based singularity detection method is proposed, which automatically calculates the Lipschitz exponent, a measure to describe the local transient activities in the measurement signal. The relationship between the Lipschitz exponent and the type and severity of faults occurring on the grid and in the power electronics is explored. The proposed algorithms are tested and validated using simulation data from computer simulations of a doubly-fed induction generator (DFIG) wind turbine with a grid connection. A field-programmable gate array (FPGA) embedded in the system has been utilised, allowing the signal processing tasks to be undertaken in real-time for monitoring purposes. The paper demonstrates that a fault signal of small magnitude generated at the early stage of a fault carries the same Lipschitz exponent as the signal of large magnitude generated at the late stage of the fault, thereby providing an early warning before the fault develops into a detrimental one.

AB - Electrical condition monitoring (CM) normally involves the collection of high-frequency, instantaneous data for feature extraction. This paper presents a novel development of an electrical condition monitoring system for wind turbines. The system is developed based upon a control and data acquisition system, for which hardware modules can be configured for a particular set of signals, thus tailoring the system to a specific range of monitoring tasks. A wavelet-based singularity detection method is proposed, which automatically calculates the Lipschitz exponent, a measure to describe the local transient activities in the measurement signal. The relationship between the Lipschitz exponent and the type and severity of faults occurring on the grid and in the power electronics is explored. The proposed algorithms are tested and validated using simulation data from computer simulations of a doubly-fed induction generator (DFIG) wind turbine with a grid connection. A field-programmable gate array (FPGA) embedded in the system has been utilised, allowing the signal processing tasks to be undertaken in real-time for monitoring purposes. The paper demonstrates that a fault signal of small magnitude generated at the early stage of a fault carries the same Lipschitz exponent as the signal of large magnitude generated at the late stage of the fault, thereby providing an early warning before the fault develops into a detrimental one.

KW - Reliability

KW - Condition monitoring

KW - Operation and maintenance

KW - Real-time simulation

KW - Wind turbines

U2 - 10.1784/insi.2016.58.12.663

DO - 10.1784/insi.2016.58.12.663

M3 - Journal article

VL - 58

SP - 663

EP - 670

JO - Insight – Non-Destructive Testing and Condition Monitoring

JF - Insight – Non-Destructive Testing and Condition Monitoring

SN - 1354-2575

IS - 12

ER -