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Characteristic Analysis of DFIG Wind Turbine under Blade Mass Imbalance Fault in View of Wind Speed Spatiotemporal Distribution

Author

Listed:
  • Shuting Wan

    (Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China)

  • Kanru Cheng

    (Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China)

  • Xiaoling Sheng

    (Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China)

  • Xuan Wang

    (Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China)

Abstract

The blade mass imbalance fault is one of the common faults of the DFIG (Doubly-Fed Induction Generator) wind turbines (WTs). In this paper, considering the spatiotemporal distribution of natural wind speed and the influence of wind shear and tower shadow effect, the influence of blade mass imbalance faults on the electrical characteristics of DFIG WTs is analyzed. Firstly, the analytical expressions and variation characteristics of electromagnetic torque and electromagnetic power under blade mass imbalance are derived before and after consideration of the spatiotemporal distribution of wind speed. Then simulations on the MATLAB/Simulink platform were done to verify the theoretical analysis results. The theoretical analysis and simulation results show that, considering the spatiotemporal distribution of wind speed and the influence of wind shear and tower shadow effect, the blade mass imbalance fault will cause fluctuation at the frequency of 1P (P = the frequency of rotor rotation), 3P, and 6P on electromagnetic power. Fluctuation at 1P is caused by mass imbalance while fluctuation at 3P and 6P are caused by wind speed spatiotemporal distribution; the amplitude of fluctuation at 1P is proportional to the degree of the imbalance fault. Since the equivalent wind speed has been used in this paper instead of the average wind speed, the data is more suitable for the actual operation of the WT in the natural world and can be applied for fault diagnosis in field WT operation.

Suggested Citation

  • Shuting Wan & Kanru Cheng & Xiaoling Sheng & Xuan Wang, 2019. "Characteristic Analysis of DFIG Wind Turbine under Blade Mass Imbalance Fault in View of Wind Speed Spatiotemporal Distribution," Energies, MDPI, vol. 12(16), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3178-:d:258916
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    References listed on IDEAS

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    1. Arkaitz Rabanal & Alain Ulazia & Gabriel Ibarra-Berastegi & Jon Sáenz & Unai Elosegui, 2018. "MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms," Energies, MDPI, vol. 12(1), pages 1-19, December.
    2. Unai Elosegui & Igor Egana & Alain Ulazia & Gabriel Ibarra-Berastegi, 2018. "Pitch Angle Misalignment Correction Based on Benchmarking and Laser Scanner Measurement in Wind Farms," Energies, MDPI, vol. 11(12), pages 1-20, December.
    3. Davide Astolfi & Francesco Castellani, 2019. "Wind Turbine Power Curve Upgrades: Part II," Energies, MDPI, vol. 12(8), pages 1-20, April.
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    Cited by:

    1. 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.
    2. Tania García-Sánchez & Irene Muñoz-Benavente & Emilio Gómez-Lázaro & Ana Fernández-Guillamón, 2020. "Modelling Types 1 and 2 Wind Turbines Based on IEC 61400-27-1: Transient Response under Voltage Dips," Energies, MDPI, vol. 13(16), pages 1-19, August.

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