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An Improved Power Control Approach for Wind Turbine Fatigue Balancing in an Offshore Wind Farm

Author

Listed:
  • Rongyong Zhao

    (School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China)

  • Daheng Dong

    (School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China)

  • Cuiling Li

    (School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China)

  • Steven Liu

    (Institute of Control Systems, University of Kaiserslautern, 67663 Kaiserslautern, Germany)

  • Hao Zhang

    (School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China)

  • Miyuan Li

    (School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China)

  • Wenzhong Shen

    (Department of Wind Energy, Technical University of Denmark, DK-2800 Lyngby, Denmark)

Abstract

Increasing maintenance costs will hinder the expansion of the wind power industry in the coming decades. Training personnel, field maintenance, and frequent boat or helicopter visits to wind turbines (WTs) is becoming a large cost. One reason for this cost is the routine turbine inspection repair and other stochastic maintenance necessitated by increasingly unbalanced figure loads and unequal turbine fatigue distribution in large-scale offshore wind farms (OWFs). In order to solve the problems of unbalanced fatigue loads and unequal turbine fatigue distribution, thereby cutting the maintenance cost, this study analyzes the disadvantages of conventional turbine fatigue definitions. We propose an improved fatigue definition that simultaneously considers the mean wind speed, wind wake turbulence, and electric power generation. Further, based on timed automata theory, a power dispatch approach is proposed to balance the fatigue loads on turbines in a wind farm. A control topology is constructed to describe the logical states of the wind farm main controller (WFMC) in an offshore wind farm. With this novel power control approach, the WFMC can re-dispatch the reference power to the wind turbines according to their cumulative fatigue value and the real wind conditions around the individual turbines in every power dispatch time interval. A workflow is also designed for the control approach implementation. Finally, to validate this proposed approach, wind data from the Horns Rev offshore wind farm in Denmark are used for a numerical simulation. All the simulation results with 3D and 2D figures illustrate that this approach is feasible to balance the loads on an offshore wind farm. Some significant implications are that this novel approach can cut the maintenance cost and also prolong the service life of OWFs.

Suggested Citation

  • Rongyong Zhao & Daheng Dong & Cuiling Li & Steven Liu & Hao Zhang & Miyuan Li & Wenzhong Shen, 2020. "An Improved Power Control Approach for Wind Turbine Fatigue Balancing in an Offshore Wind Farm," Energies, MDPI, vol. 13(7), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1549-:d:337391
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    References listed on IDEAS

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    1. Mäkitie, Tuukka & Normann, Håkon E. & Thune, Taran M. & Sraml Gonzalez, Jakoba, 2019. "The green flings: Norwegian oil and gas industry’s engagement in offshore wind power," Energy Policy, Elsevier, vol. 127(C), pages 269-279.
    2. Njiri, Jackson G. & Beganovic, Nejra & Do, Manh H. & Söffker, Dirk, 2019. "Consideration of lifetime and fatigue load in wind turbine control," Renewable Energy, Elsevier, vol. 131(C), pages 818-828.
    3. Marino, Enzo & Giusti, Alessandro & Manuel, Lance, 2017. "Offshore wind turbine fatigue loads: The influence of alternative wave modeling for different turbulent and mean winds," Renewable Energy, Elsevier, vol. 102(PA), pages 157-169.
    4. Kusiak, Andrew & Li, Wenyan & Song, Zhe, 2010. "Dynamic control of wind turbines," Renewable Energy, Elsevier, vol. 35(2), pages 456-463.
    5. Sarker, Bhaba R. & Faiz, Tasnim Ibn, 2016. "Minimizing maintenance cost for offshore wind turbines following multi-level opportunistic preventive strategy," Renewable Energy, Elsevier, vol. 85(C), pages 104-113.
    6. Ziegler, Lisa & Voormeeren, Sven & Schafhirt, Sebastian & Muskulus, Michael, 2016. "Design clustering of offshore wind turbines using probabilistic fatigue load estimation," Renewable Energy, Elsevier, vol. 91(C), pages 425-433.
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    Cited by:

    1. Denis Sidorov & Fang Liu & Yonghui Sun, 2020. "Machine Learning for Energy Systems," Energies, MDPI, vol. 13(18), pages 1-6, September.
    2. Brooks, Sam & Mahmood, Minhal & Roy, Rajkumar & Manolesos, Marinos & Salonitis, Konstantinos, 2023. "Self-reconfiguration simulations of turbines to reduce uneven farm degradation," Renewable Energy, Elsevier, vol. 206(C), pages 1301-1314.

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