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A novel optimizing power control strategy for centralized wind farm control system

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  • Ebrahimi, F.M.
  • Khayatiyan, A.
  • Farjah, E.

Abstract

The wind energy industry has grown very fast among the developed countries as it is remarkably clean and has a better quality in power system design. Consequently, with the increase of wind power applications in power systems, wind farms are needed for participation in network operations using efficient control strategies. This paper offers a comparative study on the performance of wind farm system equipped with doubly fed induction generators (DFIG) wind turbine. The study concentrates on the wind farm controller, which distributes power reference among wind turbines based on the demands of operator control system, while reducing their structural loads. The proposed controller is based on optimization technique in order to minimize the destructive fluctuation of torque and generator speed, in addition to supplying a satisfactory quality of power. The suggested wind farm control system has a hierarchical structure with both a central control level and a local control level. The performance of the control strategies is evaluated through simulations of wind farm controller equipped with DFIG wind turbines and proper results in solving the problem of both power and load optimizations are illustrated.

Suggested Citation

  • Ebrahimi, F.M. & Khayatiyan, A. & Farjah, E., 2016. "A novel optimizing power control strategy for centralized wind farm control system," Renewable Energy, Elsevier, vol. 86(C), pages 399-408.
    • Handle: RePEc:eee:renene:v:86:y:2016:i:c:p:399-408
    DOI: 10.1016/j.renene.2015.07.101
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    References listed on IDEAS

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    3. Van-Hai Bui & Akhtar Hussain & Woon-Gyu Lee & Hak-Man Kim, 2019. "Multi-Objective Optimization for Determining Trade-Off between Output Power and Power Fluctuations in Wind Farm System," Energies, MDPI, vol. 12(22), pages 1-18, November.
    4. Peng Sun & Jian Li & Junsheng Chen & Xiao Lei, 2016. "A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data," Energies, MDPI, vol. 9(11), pages 1-21, October.
    5. Mingcan Li & Hanbin Xiao & Lin Pan & Chengjun Xu, 2019. "Study of Generalized Interaction Wake Models Systems with ELM Variation for Off-Shore Wind Farms," Energies, MDPI, vol. 12(5), pages 1-32, March.
    6. Deepu Dilip & Fernando Porté-Agel, 2017. "Wind Turbine Wake Mitigation through Blade Pitch Offset," Energies, MDPI, vol. 10(6), pages 1-17, May.
    7. Gionfra, Nicolò & Sandou, Guillaume & Siguerdidjane, Houria & Faille, Damien & Loevenbruck, Philippe, 2019. "Wind farm distributed PSO-based control for constrained power generation maximization," Renewable Energy, Elsevier, vol. 133(C), pages 103-117.
    8. Peizhao Hong & Zhijun Qin, 2022. "Distributed Active Power Optimal Dispatching of Wind Farm Cluster Considering Wind Power Uncertainty," Energies, MDPI, vol. 15(7), pages 1-16, April.
    9. Isa, Normazlina Mat & Tan, Chee Wei & Yatim, A.H.M., 2018. "A comprehensive review of cogeneration system in a microgrid: A perspective from architecture and operating system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2236-2263.
    10. Minan Tang & Wenjuan Wang & Jiandong Qiu & Detao Li & Linyuan Lei, 2022. "Active Power Cooperative Control for Wind Power Clusters with Multiple Temporal and Spatial Scales," Energies, MDPI, vol. 15(24), pages 1-21, December.
    11. Dongmyoung Kim & Taesu Jeon & Insu Paek & Daeyoung Kim, 2022. "A Study on Available Power Estimation Algorithm and Its Validation," Energies, MDPI, vol. 15(7), pages 1-14, April.
    12. Cardoso, J.G. & Casella, I.R.S. & Filho, A.J. Sguarezi & Costa, F.F. & Capovilla, C.E., 2016. "SCIG wind turbine wireless controlled using morphological filtering for power quality enhancement," Renewable Energy, Elsevier, vol. 92(C), pages 303-311.

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