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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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    1. Bottasso, C.L. & Riboldi, C.E.D., 2014. "Estimation of wind misalignment and vertical shear from blade loads," Renewable Energy, Elsevier, vol. 62(C), pages 293-302.
    2. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
    3. Martin, Rebecca & Lazakis, Iraklis & Barbouchi, Sami & Johanning, Lars, 2016. "Sensitivity analysis of offshore wind farm operation and maintenance cost and availability," Renewable Energy, Elsevier, vol. 85(C), pages 1226-1236.
    4. Song, Dongran & Fan, Xinyu & Yang, Jian & Liu, Anfeng & Chen, Sifan & Joo, Young Hoon, 2018. "Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method," Applied Energy, Elsevier, vol. 224(C), pages 267-279.
    5. Taslimi-Renani, Ehsan & Modiri-Delshad, Mostafa & Elias, Mohamad Fathi Mohamad & Rahim, Nasrudin Abd., 2016. "Development of an enhanced parametric model for wind turbine power curve," Applied Energy, Elsevier, vol. 177(C), pages 544-552.
    6. Yan Pei & Zheng Qian & Bo Jing & Dahai Kang & Lizhong Zhang, 2018. "Data-Driven Method for Wind Turbine Yaw Angle Sensor Zero-Point Shifting Fault Detection," Energies, MDPI, vol. 11(3), pages 1-14, March.
    7. Ouyang, Tinghui & Kusiak, Andrew & He, Yusen, 2017. "Predictive model of yaw error in a wind turbine," Energy, Elsevier, vol. 123(C), pages 119-130.
    8. Yang, Wenxian & Court, Richard & Jiang, Jiesheng, 2013. "Wind turbine condition monitoring by the approach of SCADA data analysis," Renewable Energy, Elsevier, vol. 53(C), pages 365-376.
    9. Dai, Juchuan & Yang, Xin & Hu, Wei & Wen, Li & Tan, Yayi, 2018. "Effect investigation of yaw on wind turbine performance based on SCADA data," Energy, Elsevier, vol. 149(C), pages 684-696.
    10. Thapar, Vinay & Agnihotri, Gayatri & Sethi, Vinod Krishna, 2011. "Critical analysis of methods for mathematical modelling of wind turbines," Renewable Energy, Elsevier, vol. 36(11), pages 3166-3177.
    11. Jeong, Min-Soo & Kim, Sang-Woo & Lee, In & Yoo, Seung-Jae & Park, K.C., 2013. "The impact of yaw error on aeroelastic characteristics of a horizontal axis wind turbine blade," Renewable Energy, Elsevier, vol. 60(C), pages 256-268.
    12. Shuting Wan & Lifeng Cheng & Xiaoling Sheng, 2015. "Effects of Yaw Error on Wind Turbine Running Characteristics Based on the Equivalent Wind Speed Model," Energies, MDPI, vol. 8(7), pages 1-16, June.
    13. Cortina, G. & Sharma, V. & Calaf, M., 2017. "Investigation of the incoming wind vector for improved wind turbine yaw-adjustment under different atmospheric and wind farm conditions," Renewable Energy, Elsevier, vol. 101(C), pages 376-386.
    14. Tian, Wenlong & VanZwieten, James H. & Pyakurel, Parakram & Li, Yanjun, 2016. "Influences of yaw angle and turbulence intensity on the performance of a 20 kW in-stream hydrokinetic turbine," Energy, Elsevier, vol. 111(C), pages 104-116.
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    Cited by:

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    2. 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.
    3. 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.
    4. 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).
    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. Amira Elkodama & Amr Ismaiel & A. Abdellatif & S. Shaaban & Shigeo Yoshida & Mostafa A. Rushdi, 2023. "Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review," Energies, MDPI, vol. 16(17), pages 1-32, September.
    7. 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).

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