A comparative study on fuzzy damping controller for DFIG wind farms to improve power system oscillations

Solat, A ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.3233/JIFS-181524
  3. Publisher: IOS Press , 2019
  4. Abstract:
  5. Doubly-fed induction generator (DFIG) is the most commonly used technology for wind power generation due to the variable speed performance, decoupled control of active and reactive powers, and high efficiency. However, the DFIG originally cannot participate in damping of power system oscillations since it is not synchronously connected to the power system. This paper proposes an optimal and robust additional damping controller for the DFIG wind turbine to contribute it to damp power system oscillations. It is a fuzzy logic controller that its parameters are optimally tuned using the genetic algorithm (GA). The proposed controller modifies the DFIG active power output by using feedback from grid oscillations. Here, a comparative study is carried out for different feedback signals to determine the best of them. Comparing the results reveal that the rotor speed difference of synchronous generators is the best feedback signal to damp power system oscillations. Time domain simulations also confirm the effectiveness and robustness of the proposed controller under both the small and large disturbances. © 2019 - IOS Press and the authors. All rights reserved
  6. Keywords:
  7. damping controller ; doubly-fed induction generator ; Fuzzy logic ; power system oscillations ; Asynchronous generators ; Computer circuits ; Controllers ; Damping ; Electric fault currents ; Electric power generation ; Electric power system control ; Feedback ; Genetic algorithms ; Time domain analysis ; Wind power ; Active power output ; Comparative studies ; Damping controllers ; Doubly fed induction generator (DFIG) ; Doubly fed induction generators ; Fuzzy logic controllers ; Time-domain simulations ; Electric machine control
  8. Source: Journal of Intelligent and Fuzzy Systems ; Volume 37, Issue 4 , 2019 , Pages 4965-4978 ; 10641246 (ISSN)
  9. URL: https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs181524