Reactive power control of grid-connected wind farm based on adaptive dynamic programming

  • Authors:

  • Yufei Tang;Haibo He;Zhen Ni;Jinyu Wen;Xianchao Sui

  • Affiliations:

  • Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA;Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA;Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA;College of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;College of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

  • Venue:
  • Neurocomputing
  • Year:
  • 2014

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Abstract

Optimal control of large-scale wind farm has become a critical issue for the development of renewable energy systems and their integration into the power grid to provide reliable, secure, and efficient electricity. Among many enabling technologies, the latest research results from both the power and energy community and computational intelligence (CI) community have demonstrated that CI research could provide key technical innovations into this challenging problem. In this paper, a neural network based controller is presented for the reactive power control of wind farm with doubly fed induction generators (DFIG). Specifically, we investigate the on-line learning and control approach based on adaptive dynamic programming (ADP) for wind farm control and integration with the grid. This controller can effectively dampen the oscillation of the wind farm system after the ground fault of the grid. Compared to previous control strategies, this controller is on-line and ''model free'', and therefore, can reduce the control complexity. Simulation studies are carried out in Matlab/Simulink and the results demonstrated the effectiveness of the ADP controller.