Cover Image

Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators

This study investigates strategies for enhancing the performance of dual-star induction generators in wind power systems by optimizing the full control algorithm. The control mechanisms involved include the PID (Proportional-Integral-Derivative) controller for speed regulation and the PI (Proportion...

Full description

Saved in:
Bibliographic Details
Main Authors: Benamara K., Amimeur H., Hamoudi Y., Abdolrasol M.G.M., Cali U., Ustun T.S.
Other Authors: 59404373900
Format: Article
Published: Frontiers Media SA 2025
Subjects:
Asynchronous generators
DC generators
Invariance
Linear programming
Optimal control systems
Proportional control systems
Speed regulators
Three term control systems
Two term control systems
Windmill
Dual star induction generator
Energy
Field-oriented control
Gray wolf optimization
Gray wolves
Optimisations
Particle swarm
Particle swarm optimization
Swarm optimization
Wind power systems
Particle swarm optimization (PSO)
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-36915
record_format dspace
spelling my.uniten.dspace-369152025-03-03T15:45:44Z Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators Benamara K. Amimeur H. Hamoudi Y. Abdolrasol M.G.M. Cali U. Ustun T.S. 59404373900 24528376900 58420428800 35796848700 54974113000 43761679200 Asynchronous generators DC generators Invariance Linear programming Optimal control systems Proportional control systems Speed regulators Three term control systems Two term control systems Windmill Dual star induction generator Energy Field-oriented control Gray wolf optimization Gray wolves Optimisations Particle swarm Particle swarm optimization Swarm optimization Wind power systems Particle swarm optimization (PSO) This study investigates strategies for enhancing the performance of dual-star induction generators in wind power systems by optimizing the full control algorithm. The control mechanisms involved include the PID (Proportional-Integral-Derivative) controller for speed regulation and the PI (Proportional-Integral) controller for flux, DC-link voltage, and grid connection control. The primary objective is to optimize the entire system by fine-tuning PID and PI controllers through the application of meta-heuristic algorithms, specifically Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). These algorithms play a crucial role in estimating the optimal values of Kp, Ki, and Kd for the PID speed controller, as well as Kp and Ki for the PI controller used in the flux, DC-link voltage, and grid connection for wind energy conversion system based dual-star induction generator. This comprehensive optimization ensures accurate parameter tuning for optimal system performance. A comparative analysis of the optimization results has been conducted, focusing on the outcomes obtained with the GWO algorithm. The findings reveal a notable reduction in steady-state error, signifying improved stability, and an overall enhancement in the wind power system?s performance. This study contributes valuable insights into the effective application of meta-heuristic algorithms for optimizing dual-star induction generators in wind power systems. Copyright ? 2024 Benamara, Amimeur, Hamoudi, Abdolrasol, Cali and Ustun. Final 2025-03-03T07:45:44Z 2025-03-03T07:45:44Z 2024 Article 10.3389/fenrg.2024.1421336 2-s2.0-85208606588 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208606588&doi=10.3389%2ffenrg.2024.1421336&partnerID=40&md5=68303a6b6479a953a04b760eba0ab785 https://irepository.uniten.edu.my/handle/123456789/36915 12 1421336 All Open Access; Gold Open Access Frontiers Media SA Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Asynchronous generators
DC generators
Invariance
Linear programming
Optimal control systems
Proportional control systems
Speed regulators
Three term control systems
Two term control systems
Windmill
Dual star induction generator
Energy
Field-oriented control
Gray wolf optimization
Gray wolves
Optimisations
Particle swarm
Particle swarm optimization
Swarm optimization
Wind power systems
Particle swarm optimization (PSO)
spellingShingle Asynchronous generators
DC generators
Invariance
Linear programming
Optimal control systems
Proportional control systems
Speed regulators
Three term control systems
Two term control systems
Windmill
Dual star induction generator
Energy
Field-oriented control
Gray wolf optimization
Gray wolves
Optimisations
Particle swarm
Particle swarm optimization
Swarm optimization
Wind power systems
Particle swarm optimization (PSO)
Benamara K.
Amimeur H.
Hamoudi Y.
Abdolrasol M.G.M.
Cali U.
Ustun T.S.
Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators
description This study investigates strategies for enhancing the performance of dual-star induction generators in wind power systems by optimizing the full control algorithm. The control mechanisms involved include the PID (Proportional-Integral-Derivative) controller for speed regulation and the PI (Proportional-Integral) controller for flux, DC-link voltage, and grid connection control. The primary objective is to optimize the entire system by fine-tuning PID and PI controllers through the application of meta-heuristic algorithms, specifically Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). These algorithms play a crucial role in estimating the optimal values of Kp, Ki, and Kd for the PID speed controller, as well as Kp and Ki for the PI controller used in the flux, DC-link voltage, and grid connection for wind energy conversion system based dual-star induction generator. This comprehensive optimization ensures accurate parameter tuning for optimal system performance. A comparative analysis of the optimization results has been conducted, focusing on the outcomes obtained with the GWO algorithm. The findings reveal a notable reduction in steady-state error, signifying improved stability, and an overall enhancement in the wind power system?s performance. This study contributes valuable insights into the effective application of meta-heuristic algorithms for optimizing dual-star induction generators in wind power systems. Copyright ? 2024 Benamara, Amimeur, Hamoudi, Abdolrasol, Cali and Ustun.
author2 59404373900
author_facet 59404373900
Benamara K.
Amimeur H.
Hamoudi Y.
Abdolrasol M.G.M.
Cali U.
Ustun T.S.
format Article
author Benamara K.
Amimeur H.
Hamoudi Y.
Abdolrasol M.G.M.
Cali U.
Ustun T.S.
author_sort Benamara K.
title Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators
title_short Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators
title_full Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators
title_fullStr Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators
title_full_unstemmed Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators
title_sort grey wolf optimization for enhanced performance in wind power system with dual-star induction generators
publisher Frontiers Media SA
publishDate 2025
_version_ 1825816302741094400
score 13.244109

Similar Items