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Effects of Yaw Error on Wind Turbine Running Characteristics Based on the Equivalent Wind Speed Model

Author

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  • Shuting Wan

    (School of Energy Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China)

  • Lifeng Cheng

    (School of Energy Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China)

  • Xiaoling Sheng

    (School of Energy Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China
    Department of Electrical Engineering, North China Electric Power University, Baoding 071003, China)

Abstract

Natural wind is stochastic, being characterized by its speed and direction which change randomly and frequently. Because of the certain lag in control systems and the yaw body itself, wind turbines cannot be accurately aligned toward the wind direction when the wind speed and wind direction change frequently. Thus, wind turbines often suffer from a series of engineering issues during operation, including frequent yaw, vibration overruns and downtime. This paper aims to study the effects of yaw error on wind turbine running characteristics at different wind speeds and control stages by establishing a wind turbine model, yaw error model and the equivalent wind speed model that includes the wind shear and tower shadow effects. Formulas for the relevant effect coefficients T c , S c and P c were derived. The simulation results indicate that the effects of the aerodynamic torque, rotor speed and power output due to yaw error at different running stages are different and that the effect rules for each coefficient are not identical when the yaw error varies. These results may provide theoretical support for optimizing the yaw control strategies for each stage to increase the running stability of wind turbines and the utilization rate of wind energy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:7:p:6286-6301:d:51639
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    References listed on IDEAS

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    Cited by:

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    2. 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.
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    4. 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.
    5. Momeni, Farhang & Sabzpoushan, Seyedali & Valizadeh, Reza & Morad, Mohammad Reza & Liu, Xun & Ni, Jun, 2019. "Plant leaf-mimetic smart wind turbine blades by 4D printing," Renewable Energy, Elsevier, vol. 130(C), pages 329-351.
    6. 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.
    7. Cho, Whang & Lee, Kooksun & Choy, Ick & Back, Juhoon, 2017. "Development and experimental verification of counter-rotating dual rotor/dual generator wind turbine: Generating, yawing and furling," Renewable Energy, Elsevier, vol. 114(PB), pages 644-654.
    8. Liu, Yongqian & Qiao, Yanhui & Han, Shuang & Tao, Tao & Yan, Jie & Li, Li & Bekhbat, Galsan & Munkhtuya, Erdenebat, 2021. "Rotor equivalent wind speed calculation method based on equivalent power considering wind shear and tower shadow," Renewable Energy, Elsevier, vol. 172(C), pages 882-896.
    9. Dai, Juchuan & He, Tao & Li, Mimi & Long, Xin, 2021. "Performance study of multi-source driving yaw system for aiding yaw control of wind turbines," Renewable Energy, Elsevier, vol. 163(C), pages 154-171.
    10. Tanvir Ahmad & Abdul Basit & Juveria Anwar & Olivier Coupiac & Behzad Kazemtabrizi & Peter C. Matthews, 2019. "Fast Processing Intelligent Wind Farm Controller for Production Maximisation," Energies, MDPI, vol. 12(3), pages 1-17, February.
    11. 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).
    12. Jong-Hyeon Shin & Jong-Hwi Lee & Se-Myong Chang, 2019. "A Simplified Numerical Model for the Prediction of Wake Interaction in Multiple Wind Turbines," Energies, MDPI, vol. 12(21), pages 1-14, October.
    13. Ahmed G. Abo-Khalil & Saeed Alyami & Khairy Sayed & Ayman Alhejji, 2019. "Dynamic Modeling of Wind Turbines Based on Estimated Wind Speed under Turbulent Conditions," Energies, MDPI, vol. 12(10), pages 1-25, May.
    14. Xiaoling Sheng & Shuting Wan & Kanru Cheng & Xuan Wang, 2020. "Research on the Fault Characteristic of Wind Turbine Generator System Considering the Spatiotemporal Distribution of the Actual Wind Speed," Energies, MDPI, vol. 13(2), pages 1-16, January.
    15. Ambach, Daniel & Schmid, Wolfgang, 2017. "A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting," Energy, Elsevier, vol. 135(C), pages 833-850.

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