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Research on Wind Turbines Fault Diagnosis Technology Based on CMS Data Feature Extraction

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Abstract

As a rich clean and environmentally friendly renewable resources, wind energy has emerged as a strategic choice for countries around the world. Because the wind turbines often operate in severe working conditions such as variable load and large temperature difference they are prone to failures and possible shutdowns. The shutdowns however seriously affect the economic benefits of the wind turbines. Initiative maintenance has become a worldwide recognized scientific method for planning and determining preventive maintenance work, the implementation of this strategy relies on real-time condition monitoring and fault signal identification methods. The condition monitoring of wind turbine can help master the health state and power generation performance of wind turbine, so as to timely formulate maintenance strategies and adopt technical modification measures to improve power generation performance, reduce the down time of wind turbine, avoid the occurrence of major faults, save maintenance cost and improve power generation capacity. Therefore, a condition monitoring system is built on a wind turbine of Zhangjiakou, and a systematic signal analysis method is proposed, time-domain synchronous averaging technology, based on variable period, impulse signal feature extraction technology based on Teager and signal decomposition technology based on CEEMD. The proposed method realizes the signal analysis and feature extraction of non-stationary nonlinear, weak signal and frequency aliasing signals, and successfully diagnose the gearbox secondary meshing failure during the long-term monitoring. This confirms that the monitoring system methods and signal analysis technology proposed in this paper can effectively realize the condition monitoring and fault diagnosis of wind turbines.

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Acknowledgement

This work was supported by the National Key Research and Development Program of China(2018YFB0904005), and Science and Technology Project of State Grid (SGDK0000NYJS1803505).

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Authors and Affiliations

  1. State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems (China Electric Power Research Institute), Beijing, 100192, China

    Shiyao QIN, Ruiming WANG & Deyi FU

Authors
  1. Shiyao QIN
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  2. Ruiming WANG
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  3. Deyi FU
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Contributions

CONCEPTION: QIN Shiyao, and FU Deyi. INTERPRETATION OR ANALYSIS OF DATA: QIN Shiyao. PREPARATION OF THE MANUSCRIPT: WANG Ruiming. REVISION FOR IMPORTANT INTELLECTUAL CONTENT: WANG Ruiming. SUPERVISION: QIN Shiyao, and FU Deyi.

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Correspondence to Deyi FU.

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The author(s) declared no potential conflicts of interest with respect to the research, author- ship, and/or publication of this article.

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The datasets used and analyzed during the current study are available from the corresponding author on reasonable request

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QIN, S., WANG, R. & FU, D. Research on Wind Turbines Fault Diagnosis Technology Based on CMS Data Feature Extraction. Wireless Pers Commun 127, 271–291 (2022). https://doi.org/10.1007/s11277-021-08261-1

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