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Investigation of the Design and Fault Prediction Method for an Abrasive Particle Sensor Used in Wind Turbine Gearbox

Author

Listed:
  • Le Zhang

    (Jiangsu Key Construction Laboratory of IoT Application Technology, Wuxi Taihu University, Wuxi 214000, China)

  • Qiang Yang

    (Jiangsu Key Construction Laboratory of IoT Application Technology, Wuxi Taihu University, Wuxi 214000, China)

Abstract

The gearbox is a key sub-component of a wind power generation system with high failure rate leading to shutdowns. By monitoring the abrasive particles in the lubricating oil when the gearbox is running, any abnormal condition of the gearbox can be found in advance. This information may be used to improve the operational safety of the wind turbine and reduce losses because of shutdowns and maintenance. In this paper, a three-coil induction abrasive particle sensor is designed based on the application of high-power wind turbine gearbox. The performance of the sensor and the design method of the detection circuit are described in detail, and the sensor operation performance used in the 2 MW wind turbine is verified. The results show that the sensor has superior performance in identifying ferromagnetic abrasive particles above 200 μm and plays a good role in status monitoring and fault prediction for the gearbox.

Suggested Citation

  • Le Zhang & Qiang Yang, 2020. "Investigation of the Design and Fault Prediction Method for an Abrasive Particle Sensor Used in Wind Turbine Gearbox," Energies, MDPI, vol. 13(2), pages 1-13, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:365-:d:307798
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    References listed on IDEAS

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    1. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
    2. Antoniadis, Anestis & Dinh Tuan Pham, 1998. "Wavelet regression for random or irregular design," Computational Statistics & Data Analysis, Elsevier, vol. 28(4), pages 353-369, October.
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    Cited by:

    1. Wang, Ziqi & Liu, Changliang & Yan, Feng, 2022. "Condition monitoring of wind turbine based on incremental learning and multivariate state estimation technique," Renewable Energy, Elsevier, vol. 184(C), pages 343-360.
    2. Junshuai Yan & Yongqian Liu & Xiaoying Ren, 2023. "An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm," Energies, MDPI, vol. 16(10), pages 1-23, May.

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