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GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades

Author

Listed:
  • Zheng Liu

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Xin Liu

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Kan Wang

    (China General Certification Center, Beijing 100020, China)

  • Zhongwei Liang

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • José A.F.O. Correia

    (INEGI, Faculty of Engineering, University of Porto, Porto 4200-465, Portugal)

  • Abílio M.P. De Jesus

    (INEGI, Faculty of Engineering, University of Porto, Porto 4200-465, Portugal)

Abstract

This paper proposes a strain prediction method for wind turbine blades using genetic algorithm back propagation neural networks (GA-BPNNs) with applied loads, loading positions, and displacement as inputs, and the study can be used to provide more data for the wind turbine blades’ health assessment and life prediction. Among all parameters to be tested in full-scale static testing of wind turbine blades, strain is very important. The correlation between the blade strain and the applied loads, loading position, displacement, etc., is non-linear, and the number of input variables is too much, thus the calculation and prediction of the blade strain are very complex and difficult. Moreover, the number of measuring points on the blade is limited, so the full-scale blade static test cannot usually provide enough data and information for the improvement of the blade design. As a result of these concerns, this paper studies strain prediction methods for full-scale blade static testing by introducing GA-BPNN. The accuracy and usability of the GA-BPNN prediction model was verified by the comparison with BPNN model and the FEA results. The results show that BPNN can be effectively used to predict the strain of unmeasured points of wind turbine blades.

Suggested Citation

  • Zheng Liu & Xin Liu & Kan Wang & Zhongwei Liang & José A.F.O. Correia & Abílio M.P. De Jesus, 2019. "GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades," Energies, MDPI, vol. 12(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1026-:d:214402
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    Citations

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    Cited by:

    1. Ma, Deyin & Zhang, Lizhi & Sun, Bo, 2021. "An interval scheduling method for the CCHP system containing renewable energy sources based on model predictive control," Energy, Elsevier, vol. 236(C).
    2. Xiaomei Wu & Chun Sing Lai & Chenchen Bai & Loi Lei Lai & Qi Zhang & Bo Liu, 2020. "Optimal Kernel ELM and Variational Mode Decomposition for Probabilistic PV Power Prediction," Energies, MDPI, vol. 13(14), pages 1-21, July.
    3. Yang, Xiaohui & Wang, Xiaopeng & Leng, Zhengyang & Deng, Yeheng & Deng, Fuwei & Zhang, Zhonglian & Yang, Li & Liu, Xiaoping, 2023. "An optimized scheduling strategy combining robust optimization and rolling optimization to solve the uncertainty of RES-CCHP MG," Renewable Energy, Elsevier, vol. 211(C), pages 307-325.
    4. Weiwu Feng & Da Yang & Wenxue Du & Qiang Li, 2023. "In Situ Structural Health Monitoring of Full-Scale Wind Turbine Blades in Operation Based on Stereo Digital Image Correlation," Sustainability, MDPI, vol. 15(18), pages 1-17, September.

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