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Optimal Frequency Regulation in Multi-Microgrid Systems using Federated Learning

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dc.citedby1
dc.contributor.authorIrudayaraj A.X.R.en_US
dc.contributor.authorWahab N.I.A.en_US
dc.contributor.authorVeerasamy V.en_US
dc.contributor.authorPremkumar M.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorGooi H.B.en_US
dc.contributor.authorid57216703079en_US
dc.contributor.authorid24448826700en_US
dc.contributor.authorid57201719362en_US
dc.contributor.authorid57191413142en_US
dc.contributor.authorid6602912020en_US
dc.contributor.authorid7006434142en_US
dc.date.accessioned2024-10-14T03:21:17Z
dc.date.available2024-10-14T03:21:17Z
dc.date.issued2023
dc.description.abstractThis paper proposes a Federated Learning-based Zeroing Neural Network (FL-ZNN) tuned optimal proportional-integral-derivative (PID) control strategy for frequency control of Multi-Microgrid (MMG) system. The proposed FL-ZNN technique employs a distributed learning approach that allows each neuron to train the network based on its own local data. The local models are then aggregated into a global model, which is used to update the neurons of the network to auto-tune the PID controller's parameters in each microgrid. The proposed FL-ZNN-based PID controller is able to provide robust and efficient frequency control in MMG under different operating conditions, including successive load variations and communication delay. Simulation results demonstrate the effectiveness and superiority of the proposed FL-ZNN-based control strategy over the ZNN PID, and conventional ZNN controller in terms of response time, overshoot, and settling time. Further, the proposed controller has been validated using Hardware-in-the-Loop (HIL) in OPAL-RT. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/GlobConET56651.2023.10150045
dc.identifier.scopus2-s2.0-85164254068
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85164254068&doi=10.1109%2fGlobConET56651.2023.10150045&partnerID=40&md5=a67aa7820f75b6fd7d3143f024562adb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34634
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023
dc.subjectand Load Frequency Control
dc.subjectFederated learning
dc.subjectMulti-microgrid system
dc.subjectZeroing Neural Network
dc.subjectControllers
dc.subjectElectric control equipment
dc.subjectElectric frequency control
dc.subjectElectric loads
dc.subjectLearning systems
dc.subjectMicrogrids
dc.subjectProportional control systems
dc.subjectThree term control systems
dc.subjectTwo term control systems
dc.subjectAnd load frequency control
dc.subjectControl strategies
dc.subjectFederated learning
dc.subjectLoad-frequency control
dc.subjectMicrogrid systems
dc.subjectMulti micro-grids
dc.subjectMulti-microgrid system
dc.subjectNetwork-based
dc.subjectNeural-networks
dc.subjectZeroing neural network
dc.subjectNeural networks
dc.titleOptimal Frequency Regulation in Multi-Microgrid Systems using Federated Learningen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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