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

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dc.citedby0
dc.contributor.authorBenamara K.en_US
dc.contributor.authorAmimeur H.en_US
dc.contributor.authorHamoudi Y.en_US
dc.contributor.authorAbdolrasol M.G.M.en_US
dc.contributor.authorCali U.en_US
dc.contributor.authorUstun T.S.en_US
dc.contributor.authorid59404373900en_US
dc.contributor.authorid24528376900en_US
dc.contributor.authorid58420428800en_US
dc.contributor.authorid35796848700en_US
dc.contributor.authorid54974113000en_US
dc.contributor.authorid43761679200en_US
dc.date.accessioned2025-03-03T07:45:44Z
dc.date.available2025-03-03T07:45:44Z
dc.date.issued2024
dc.description.abstractThis 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1421336
dc.identifier.doi10.3389/fenrg.2024.1421336
dc.identifier.scopus2-s2.0-85208606588
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85208606588&doi=10.3389%2ffenrg.2024.1421336&partnerID=40&md5=68303a6b6479a953a04b760eba0ab785
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36915
dc.identifier.volume12
dc.publisherFrontiers Media SAen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleFrontiers in Energy Research
dc.subjectAsynchronous generators
dc.subjectDC generators
dc.subjectInvariance
dc.subjectLinear programming
dc.subjectOptimal control systems
dc.subjectProportional control systems
dc.subjectSpeed regulators
dc.subjectThree term control systems
dc.subjectTwo term control systems
dc.subjectWindmill
dc.subjectDual star induction generator
dc.subjectEnergy
dc.subjectField-oriented control
dc.subjectGray wolf optimization
dc.subjectGray wolves
dc.subjectOptimisations
dc.subjectParticle swarm
dc.subjectParticle swarm optimization
dc.subjectSwarm optimization
dc.subjectWind power systems
dc.subjectParticle swarm optimization (PSO)
dc.titleGrey wolf optimization for enhanced performance in wind power system with dual-star induction generatorsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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