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Improving wind turbine efficiency through detection and calibration of yaw misalignment

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  • Jing, Bo
  • Qian, Zheng
  • Pei, Yan
  • Zhang, Lizhong
  • Yang, Tingyi

Abstract

Yaw misalignment has a serious impact on energy capture, power quality and health status of wind turbine. However, most detection and calibration methods require additional equipment and the detection results are seriously disturbed by the complex working conditions. In this paper, two types of typical yaw misalignments are defined at first. A new simulation method is applied to simulate the power outputs in different yaw states. Based on the simulation data, a detailed analysis of yaw misalignment effect on Wind Turbine Power Generation (WTPG) is made subsequently, and we find that different yaw misalignments have coupling effects on WTPG. According to the theoretical analysis, an improved yaw misalignment detection method based on Maximum Power Capture (MPC) is proposed, and only SCADA data is used as the model input. After detection, yaw misalignments can be easily calibrated without manual operation. Both simulation data and measured data of multiple wind turbines are used to evaluate the model performance. The results show that the proposed method can improve the efficiency of horizontal axis wind turbines by detecting and calibrating of yaw misalignment, and it has stronger robustness and wider applicability compared with other data-dirven methods.

Suggested Citation

  • Jing, Bo & Qian, Zheng & Pei, Yan & Zhang, Lizhong & Yang, Tingyi, 2020. "Improving wind turbine efficiency through detection and calibration of yaw misalignment," Renewable Energy, Elsevier, vol. 160(C), pages 1217-1227.
    • Handle: RePEc:eee:renene:v:160:y:2020:i:c:p:1217-1227
    DOI: 10.1016/j.renene.2020.07.063
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    Cited by:

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    2. Yang, Jian & Wang, Li & Song, Dongran & Huang, Chaoneng & Huang, Liansheng & Wang, Junlei, 2022. "Incorporating environmental impacts into zero-point shifting diagnosis of wind turbines yaw angle," Energy, Elsevier, vol. 238(PA).
    3. Mehlan, Felix C. & Nejad, Amir R., 2023. "Rotor imbalance detection and diagnosis in floating wind turbines by means of drivetrain condition monitoring," Renewable Energy, Elsevier, vol. 212(C), pages 70-81.
    4. Francesco Castellani & Abdelgalil Eltayesh & Matteo Becchetti & Antonio Segalini, 2021. "Aerodynamic Analysis of a Wind-Turbine Rotor Affected by Pitch Unbalance," Energies, MDPI, vol. 14(3), pages 1-16, January.
    5. Davide Astolfi & Ravi Pandit & Linyue Gao & Jiarong Hong, 2022. "Individuation of Wind Turbine Systematic Yaw Error through SCADA Data," Energies, MDPI, vol. 15(21), pages 1-5, November.
    6. Zhu, Xiaoxun & Chen, Yao & Xu, Shinai & Zhang, Shaohai & Gao, Xiaoxia & Sun, Haiying & Wang, Yu & Zhao, Fei & Lv, Tiancheng, 2023. "Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification," Energy, Elsevier, vol. 270(C).
    7. Ravi Kumar Pandit & Davide Astolfi & Isidro Durazo Cardenas, 2023. "A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines," Energies, MDPI, vol. 16(4), pages 1-17, February.

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