Volume 9, Issue 3 (September 2013)                   IJEEE 2013, 9(3): 167-176 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Ghazi R, Khajeh A. GA-Based Optimal LQR Controller to Improve LVRT Capability of DFIG Wind Turbines. IJEEE 2013; 9 (3) :167-176
URL: http://ijeee.iust.ac.ir/article-1-569-en.html
Abstract:   (13175 Views)
Nowadays, the doubly-fed induction generators (DFIGs) based wind turbines (WTs) are the dominant type of WTs connected to grid. Traditionally the back-to-back converters are used to control the DFIGs. In this paper, an Indirect Matrix Converter (IMC) is proposed to control the generator. Compared with back-to-back converters, IMCs have numerous advantages such as: higher level of robustness, reliability, reduced size and weight due to the absence of bulky electrolytic capacitor. According to the recent grid codes it is required that wind turbines remain connected to the grid during grid faults and following voltage dips. This feature is called low voltage ride-through (LVRT) capability. In this paper the linear quadratic regulator (LQR) controller is used for optimal control of the DFIG. The weighting matrices of the LQR are obtained using the genetic algorithm (GA) technique. With the LQR controller the intention is to improve the LVRT capability of the DFIG wind turbines to satisfy the new LVRT requirements. Compared to the PI controller, the superiority of the LQR controller in improving the transient stability and LVRT performance of the DFIG wind turbines is evident. Simulation results confirm the efficiency of the proposed controller.
Full-Text [PDF 423 kb]   (4185 Downloads)    
Type of Study: Research Paper | Subject: Renewable Generation
Received: 2013/04/16 | Revised: 2014/09/28 | Accepted: 2013/07/22

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.