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Acoustical damage detection of wind turbine yaw system using Bayesian network

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  • Chen, Bin
  • Xie, Lei
  • Li, Yongzhan
  • Gao, Baocheng

Abstract

Yaw system plays a significant role in increasing wind power production and protecting the wind turbine. However, the working yaw system suffers from complex alternating stresses and could result in failure and significant economic losses. This paper develops an acoustical damage detection method of the yaw system based on Bayesian network (BN). In the method, the sound pressure level (SPL) features are first extracted from the measuring acoustic signal to characterize the state of yaw system. Subsequently, a data discretization method based on self-organizing map and information gain rate is proposed to convert continuous SPL features into a finite set of intervals with respect to attribute values. Besides, a three-layer BN diagnostic model combined with the structure learning strategy based on Bayesian information criterion is designed for damage detection of the yaw system. Finally, experiments are conducted in practical wind farm to validate the feasibility and efficiency of the proposed method.

Suggested Citation

  • Chen, Bin & Xie, Lei & Li, Yongzhan & Gao, Baocheng, 2020. "Acoustical damage detection of wind turbine yaw system using Bayesian network," Renewable Energy, Elsevier, vol. 160(C), pages 1364-1372.
    • Handle: RePEc:eee:renene:v:160:y:2020:i:c:p:1364-1372
    DOI: 10.1016/j.renene.2020.07.062
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    References listed on IDEAS

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    Cited by:

    1. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    2. Luo, Kai & Chen, Liang & Liang, Wei, 2022. "Structural health monitoring of carbon fiber reinforced polymer composite laminates for offshore wind turbine blades based on dual maximum correlation coefficient method," Renewable Energy, Elsevier, vol. 201(P1), pages 1163-1175.
    3. Chen, Jian & Zhang, Yu & Xu, Zhongyun & Li, Chun, 2023. "Flow characteristics analysis and power comparison for two novel types of vertically staggered wind farms," Energy, Elsevier, vol. 263(PE).
    4. 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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