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Wind Farm Power Optimization and Fault Ride-Through under Inter-Turn Short-Circuit Fault

Author

Listed:
  • Kuichao Ma

    (Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark)

  • Mohsen Soltani

    (Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark)

  • Amin Hajizadeh

    (Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark)

  • Jiangsheng Zhu

    (SEWPG European Innovation Center ApS, 8000 Aarhus, Denmark)

  • Zhe Chen

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

Abstract

Inter-Turn Short Circuit (ITSC) fault in stator winding is a common fault in Doubly-Fed Induction Generator (DFIG)-based Wind Turbines (WTs). Improper measures in the ITSC fault affect the safety of the faulty WT and the power output of the Wind Farm (WF). This paper combines derating WTs and the power optimization of the WF to diminish the fault effect. At the turbine level, switching the derating strategy and the ITSC Fault Ride-Through (FRT) strategy is adopted to ensure that WTs safely operate under fault. At the farm level, the Particle Swarm Optimization (PSO)-based active power dispatch strategy is used to address proper power references in all of the WTs. The simulation results demonstrate the effectiveness of the proposed method. Switching the derating strategy can increase the power limit of the faulty WT, and the ITSC FRT strategy can ensure that the WT operates without excessive faulty current. The PSO-based power optimization can improve the power of the WF to compensate for the power loss caused by the faulty WT. With the proposed method, the competitiveness and the operational capacity of offshore WFs can be upgraded.

Suggested Citation

  • Kuichao Ma & Mohsen Soltani & Amin Hajizadeh & Jiangsheng Zhu & Zhe Chen, 2021. "Wind Farm Power Optimization and Fault Ride-Through under Inter-Turn Short-Circuit Fault," Energies, MDPI, vol. 14(11), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3072-:d:562117
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    References listed on IDEAS

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    1. Göçmen, Tuhfe & Laan, Paul van der & Réthoré, Pierre-Elouan & Diaz, Alfredo Peña & Larsen, Gunner Chr. & Ott, Søren, 2016. "Wind turbine wake models developed at the technical university of Denmark: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 752-769.
    2. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
    3. Zhang, Jincheng & Zhao, Xiaowei, 2020. "A novel dynamic wind farm wake model based on deep learning," Applied Energy, Elsevier, vol. 277(C).
    4. Liao, Hao & Hu, Weihao & Wu, Xiawei & Wang, Ni & Liu, Zhou & Huang, Qi & Chen, Cong & Chen, Zhe, 2020. "Active power dispatch optimization for offshore wind farms considering fatigue distribution," Renewable Energy, Elsevier, vol. 151(C), pages 1173-1185.
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    Cited by:

    1. Mojtaba Ahmadieh Khanesar & Jingyi Lu & Thomas Smith & David Branson, 2021. "Electrical Load Prediction Using Interval Type-2 Atanassov Intuitionist Fuzzy System: Gravitational Search Algorithm Tuning Approach," Energies, MDPI, vol. 14(12), pages 1-18, June.
    2. Saeedreza Jadidi & Hamed Badihi & Youmin Zhang, 2021. "Fault-Tolerant Cooperative Control of Large-Scale Wind Farms and Wind Farm Clusters," Energies, MDPI, vol. 14(21), pages 1-29, November.
    3. Jordi Cusidó & Arnau López & Mattia Beretta, 2021. "Fault-Tolerant Control of a Wind Turbine Generator Based on Fuzzy Logic and Using Ensemble Learning," Energies, MDPI, vol. 14(16), pages 1-20, August.

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