Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162085
Full metadata record
DC FieldValueLanguage
dc.contributor.authorVeerasamy, Veerapandiyanen_US
dc.contributor.authorNoor Izzri Abdul Wahaben_US
dc.contributor.authorRamachandran, Rajeswarien_US
dc.contributor.authorMohammad Lutfi Othmanen_US
dc.contributor.authorHashim Hizamen_US
dc.contributor.authorKumar, Jeevitha Satheeshen_US
dc.contributor.authorIrudayaraj, Andrew Xavier Rajen_US
dc.date.accessioned2022-10-04T02:12:46Z-
dc.date.available2022-10-04T02:12:46Z-
dc.date.issued2022-
dc.identifier.citationVeerasamy, V., Noor Izzri Abdul Wahab, Ramachandran, R., Mohammad Lutfi Othman, Hashim Hizam, Kumar, J. S. & Irudayaraj, A. X. R. (2022). Design of single- and multi-loop self-adaptive PID controller using heuristic based recurrent neural network for ALFC of hybrid power system. Expert Systems With Applications, 192, 116402-. https://dx.doi.org/10.1016/j.eswa.2021.116402en_US
dc.identifier.issn0957-4174en_US
dc.identifier.urihttps://hdl.handle.net/10356/162085-
dc.description.abstractThis paper presents a novel heuristic based recurrent Hopfield neural network (HNN) designed self-adaptive proportional-integral-derivative (PID) controller for automatic load frequency control of interconnected hybrid power system (HPS). The control problem is conceptualized as an optimization problem and solved using a heuristic optimization technique with the aim of minimizing the Lyapunov function. Initially, the energy function is formulated and the differential equations governing the dynamics of HNN are derived. Then, these dynamics are solved using hybrid particle swarm optimization-gravitational search algorithm (PSO-GSA) to obtain the initial solution. The effectiveness of the controller is tested for two-area system considering the system non-linearities and integration of plug-in-electric vehicle (PEV). Further, to improve the speed of response of the system, the cascade control scheme is proposed using the presented approach of heuristic based HNN (h-HNN). The efficacy of the method is examined in single- and multi-loop PID control of three-area HPS. The performance of propounded control schemes is compared with PSO-GSA and generalized HNN based PID controller. The results obtained show that the response of proposed controller is superior in terms of transient and steady state performance indices measured. In addition, the control effort of suggested cascade controller is much reduced compared with other controllers presented. Furthermore, the self-adaptive property of the controller is analyzed for random change in load demand and their corresponding change in gain parameters are recorded. This reveals that the proposed controller is more suitable for stable operation of modern power network with green energy technologies and PEV efficiently.en_US
dc.language.isoenen_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.rights© 2021 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleDesign of single- and multi-loop self-adaptive PID controller using heuristic based recurrent neural network for ALFC of hybrid power systemen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1016/j.eswa.2021.116402-
dc.identifier.scopus2-s2.0-85121926095-
dc.identifier.volume192en_US
dc.identifier.spage116402en_US
dc.subject.keywordsHybrid Power Systemen_US
dc.subject.keywordsAutomatic Load Frequency Controlen_US
dc.description.acknowledgementThe authors gratefully acknowledge Advanced Lightning, Power and Energy System (ALPER), Universiti Putra Malaysia for providing research fund under UPM, Malaysia Grant No. GP-GPB/2021/9706100 and UPM/800-3/3/1/GPB/2019/9671700 to carry out this research. Also, thank TEQIP-III-COE-Alternate Energy Research (AER) funded by NPIU, Government College of Technology, Tamil Nadu, India for supporting this research.en_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:EEE Journal Articles

SCOPUSTM   
Citations 20

25
Updated on Mar 25, 2024

Web of ScienceTM
Citations 20

17
Updated on Oct 25, 2023

Page view(s)

77
Updated on Mar 28, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.