An Investigation into the Utilization of Swarm Intelligence for the Design of Dual Vector and Proportional–Resonant Controllers for Regulation of Doubly Fed Induction Generators Subject to Unbalanced Grid Voltages

Kumeshan Reddy and Akshay Kumar Saha ()
Additional contact information
Kumeshan Reddy: Electrical, Electronic, and Computer Engineering, University of KwaZulu-Natal, Durban 4041, South Africa
Akshay Kumar Saha: Electrical, Electronic, and Computer Engineering, University of KwaZulu-Natal, Durban 4041, South Africa

Energies, 2022, vol. 15, issue 20, 1-36

Abstract: This work presents an investigation into the use of swarm intelligence techniques for the control of the doubly fed induction generator under unbalanced grid voltages. Swarm intelligence is a concept that was introduced in the late 20th century but has since undergone constant evolution and modifications. Similarly, the doubly fed induction generator has recently come under intense investigation. Owing to the direct grid connection of the DFIG, an unbalanced grid voltage harshly impacts its output power. Established mitigation measures include the use of the dual vector and proportional–resonant control methods. This work investigates the effectiveness of utilizing swarm intelligence for the purpose of controller gain optimization. A comparison of the application of swarm intelligence to the dual vector and proportional–resonant controllers was carried out. Three swarm intelligence techniques from across the timeline were utilized including particle swarm optimization, the bat algorithm, and the gorilla troops optimization algorithm. The system was subject to single-phase voltage dips of 5% and 10%. The results indicate that modern swarm intelligence techniques are effective at optimizing controller gains. This shows that as swarm intelligence techniques evolve, they may be suitable for use in the optimization of controller gains for numerous applications.

Keywords: particle swarm optimization; bat algorithm; gorilla troops optimization; doubly fed induction generator; stability analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/20/7476/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/20/7476/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:20:p:7476-:d:939021

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().