Volume 14, Issue 4 (December 2018)                   IJEEE 2018, 14(4): 330-341 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Ahmadi H, Rajaei A, Nayeripour M, Ghani M. A Hybrid Control Method to Improve LVRT and FRT in DFIG by Using the Multi-Objective Algorithm of Krill and the Fuzzy Logic. IJEEE 2018; 14 (4) :330-341
URL: http://ijeee.iust.ac.ir/article-1-1159-en.html
A Hybrid Control Method to Improve LVRT and FRT in DFIG by Using the Multi-Objective Algorithm of Krill and the Fuzzy Logic
H. Ahmadi , A. Rajaei , M. Nayeripour , M. Ghani
Abstract:   (4841 Views)
Considering the increasing usage of the clean and renewable energies, wind energy has been saliently improved throughout the world as one of the most desired energies. Besides, most power houses and wind turbines work based on the doubly-fed induction generator (DFIG). Based on the structure and the how-ness of DFIG connection to the grid, two cases may decrease the performance of the DFIG. These two cases are known as a fault and a low-voltage in the grid. In the present paper, a hybrid method is proposed based on the multi-objective algorithm of krill and the fuzzy controller to improve the low-voltage ride through (LVRT) and the fault ride through (FRT). In this method, first by using the optimal quantities algorithm, the PI controllers’ coefficients and two variables which are equal to the demagnetize current have been calculated for different conditions of fault and low voltage. Then, these coefficients were given to the fuzzy controller. This controller diagnosed the grid condition based on the stator voltage and then it applied the proper coefficients to the control system regarding the diagnosed condition. To test the proposed method, a DFIG is implemented by taking the best advantages of the proposed method; additionally, the system performance has been tested in fault and low voltage conditions.
Keywords: Wind Energy , DFIG , Krill Algorithm , Fuzzy Controller , FRT , LVRT
Full-Text [PDF 1526 kb]   (1837 Downloads)    
Type of Study: Research Paper | Subject: Renewable Generation
Received: 2017/08/30 | Revised: 2018/12/01 | Accepted: 2018/03/18
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.