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An integrated approach for wind turbine gearbox fatigue life prediction considering instantaneously varying load conditions

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  • Ding, Fangfang
  • Tian, Zhigang
  • Zhao, Fuqiong
  • Xu, Hao

Abstract

Wind power is a significant clean energy source. Operation & maintenance (O&M) costs account for about 25% of the cost of wind power, and it is critical to improve the reliability of wind power generators to reduce the overall cost and increase wind power competitiveness comparing to other power sources. Wind turbines are subject to instantaneously varying load due to wind turbulence, which challenges the prognostic study for predicting equipment future health conditions and remaining useful lives. With existing prognostics methods, the average constant load is typically used to approximate the varying external load. In this paper, an integrated varying-load approach is proposed for predicting wind turbine gearbox remaining useful life by specifically considering instantaneously varying external load, which is more realistic. Fatigue crack damage is focused on. The method integrates gear physical models and available health condition data, and the distribution of uncertain material parameter modeled in crack degradation process is updated via Bayesian inference once new health condition data become available. Examples are provided to demonstrate the effectiveness of the proposed varying-load approach. A comparative study is conducted between the proposed approach and existing constant-load approximation method, and the results show that the proposed varying-load approach can provide more accurate prediction.

Suggested Citation

  • Ding, Fangfang & Tian, Zhigang & Zhao, Fuqiong & Xu, Hao, 2018. "An integrated approach for wind turbine gearbox fatigue life prediction considering instantaneously varying load conditions," Renewable Energy, Elsevier, vol. 129(PA), pages 260-270.
    • Handle: RePEc:eee:renene:v:129:y:2018:i:pa:p:260-270
    DOI: 10.1016/j.renene.2018.05.074
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    References listed on IDEAS

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    1. Mingming Zhao & Jinchen Ji, 2016. "Dynamic Analysis of Wind Turbine Gearbox Components," Energies, MDPI, vol. 9(2), pages 1-18, February.
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    2. Verstraeten, Timothy & Nowé, Ann & Keller, Jonathan & Guo, Yi & Sheng, Shuangwen & Helsen, Jan, 2019. "Fleetwide data-enabled reliability improvement of wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 428-437.
    3. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    4. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    5. Compare, Michele & Baraldi, Piero & Marelli, Paolo & Zio, Enrico, 2020. "Partially observable Markov decision processes for optimal operations of gas transmission networks," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    6. Pinciroli, Luca & Baraldi, Piero & Ballabio, Guido & Compare, Michele & Zio, Enrico, 2022. "Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning," Renewable Energy, Elsevier, vol. 183(C), pages 752-763.
    7. Ming Li & Yuan Luo & Liyang Xie & Cao Tong & Chuan Chen, 2024. "Fatigue reliability assessment method for wind power gear system based on multidimensional finite element method," Journal of Risk and Reliability, , vol. 238(3), pages 540-558, June.
    8. Eric Lucas dos Santos Cabral & Mario Orestes Aguirre Gonzalez & Priscila da Cunha Jacome Vidal & Joao Florencio da Costa Junior & Rafael Monteiro de Vasconcelos & David Cassimiro de Melo & Ruan Lucas , 2024. "Optimization Models for Operations and Maintenance of Offshore Wind Turbines Based on Artificial Intelligence and Operations Research: A Systematic Literature Review," International Journal of Business and Management, Canadian Center of Science and Education, vol. 19(3), pages 1-1, June.
    9. Abdulrahman A. Alghamdi & Abdelhameed Ibrahim & El-Sayed M. El-Kenawy & Abdelaziz A. Abdelhamid, 2023. "Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm," Energies, MDPI, vol. 16(3), pages 1-30, January.
    10. W. Dheelibun Remigius & Anand Natarajan, 2022. "A review of wind turbine drivetrain loads and load effects for fixed and floating wind turbines," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 11(1), January.
    11. Wang, Jingjing & Zhao, Xian & Guo, Xiaoxin, 2019. "Optimizing wind turbine's maintenance policies under performance-based contract," Renewable Energy, Elsevier, vol. 135(C), pages 626-634.
    12. Xiangfu Zou & Jie Zhang & Jian Chen & Ognjen Orozovic & Xihua Xie & Jiejie Li, 2023. "Oil Monitoring and Fault Pre-Warning of Wind Turbine Gearbox Based on Combined Predicting Method," Sustainability, MDPI, vol. 15(4), pages 1-16, February.

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