Volume 17, Issue 2 (June 2021)                   IJEEE 2021, 17(2): 1422-1422 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Rezavandi R, A. Khaburi D, Siami M, Khosravi M, Heshmatian S. Model Predictive Control of a BCDFIG With Active and Reactive Power Control Capability for Grid-Connected Applications. IJEEE 2021; 17 (2) :1422-1422
URL: http://ijeee.iust.ac.ir/article-1-1422-en.html
Model Predictive Control of a BCDFIG With Active and Reactive Power Control Capability for Grid-Connected Applications
R. Rezavandi , D. A. Khaburi , M. Siami , M. Khosravi , S. Heshmatian
Abstract:   (2988 Views)
Recently, Brushless Cascaded Doubly Fed Induction Generator (BCDFIG) has been considered as an attractive choice for grid-connected applications due to its high controllability and reliability. In this paper, a Finite Control Set Model Predictive Control (FCS-MPC) method with active and reactive power control capability in grid-connected mode is proposed for controlling the BCDFIG in a way that notable improvement of the dynamic response, ripple reduction of the active and reactive power waveforms and also better THD performance are achieved compared to the traditional approaches such as Vector Control (VC) method. For this purpose, the required mathematical equations are obtained and presented in detail. In order to validate the proposed method performance, a 1–MW grid-connected BCDFIG is simulated in MATLAB/Simulink environment.
Keywords: Brushless Cascaded Doubly Fed Induction Generator (BCDFIG) , Dynamic Response , Predictive Control , Grid-Connected Systems
Full-Text [PDF 1817 kb]   (1147 Downloads)    
  • Effective Control of BCDFIG in grid-connected mode.
  • The active and reactive powers are appropriately exchanged with the grid.
  • Model predictive control improves the BCDFIG performance.
  • Achieving better transient response and harmonic performance.

Type of Study: Research Paper | Subject: Electrical Drives
Received: 2019/01/12 | Revised: 2020/08/01 | Accepted: 2020/08/03
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.