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Optimal Frequency Regulation in Multi-Microgrid Systems using Federated Learning

This 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...

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Main Authors:	Irudayaraj A.X.R., Wahab N.I.A., Veerasamy V., Premkumar M., Ramachandaramurthy V.K., Gooi H.B.
Other Authors:	57216703079
Format:	Conference Paper
Published:	Institute of Electrical and Electronics Engineers Inc. 2024
Subjects:	and Load Frequency Control Federated learning Multi-microgrid system Zeroing Neural Network Controllers Electric control equipment Electric frequency control Electric loads Learning systems Microgrids Proportional control systems Three term control systems Two term control systems And load frequency control Control strategies Load-frequency control Microgrid systems Multi micro-grids Network-based Neural-networks Zeroing neural network Neural networks
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id	my.uniten.dspace-34634
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spelling	my.uniten.dspace-346342024-10-14T11:21:17Z Optimal Frequency Regulation in Multi-Microgrid Systems using Federated Learning Irudayaraj A.X.R. Wahab N.I.A. Veerasamy V. Premkumar M. Ramachandaramurthy V.K. Gooi H.B. 57216703079 24448826700 57201719362 57191413142 6602912020 7006434142 and Load Frequency Control Federated learning Multi-microgrid system Zeroing Neural Network Controllers Electric control equipment Electric frequency control Electric loads Learning systems Microgrids Proportional control systems Three term control systems Two term control systems And load frequency control Control strategies Federated learning Load-frequency control Microgrid systems Multi micro-grids Multi-microgrid system Network-based Neural-networks Zeroing neural network Neural networks This 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. Final 2024-10-14T03:21:17Z 2024-10-14T03:21:17Z 2023 Conference Paper 10.1109/GlobConET56651.2023.10150045 2-s2.0-85164254068 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164254068&doi=10.1109%2fGlobConET56651.2023.10150045&partnerID=40&md5=a67aa7820f75b6fd7d3143f024562adb https://irepository.uniten.edu.my/handle/123456789/34634 Institute of Electrical and Electronics Engineers Inc. 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	and Load Frequency Control Federated learning Multi-microgrid system Zeroing Neural Network Controllers Electric control equipment Electric frequency control Electric loads Learning systems Microgrids Proportional control systems Three term control systems Two term control systems And load frequency control Control strategies Federated learning Load-frequency control Microgrid systems Multi micro-grids Multi-microgrid system Network-based Neural-networks Zeroing neural network Neural networks
spellingShingle	and Load Frequency Control Federated learning Multi-microgrid system Zeroing Neural Network Controllers Electric control equipment Electric frequency control Electric loads Learning systems Microgrids Proportional control systems Three term control systems Two term control systems And load frequency control Control strategies Federated learning Load-frequency control Microgrid systems Multi micro-grids Multi-microgrid system Network-based Neural-networks Zeroing neural network Neural networks Irudayaraj A.X.R. Wahab N.I.A. Veerasamy V. Premkumar M. Ramachandaramurthy V.K. Gooi H.B. Optimal Frequency Regulation in Multi-Microgrid Systems using Federated Learning
description	This 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.
author2	57216703079
author_facet	57216703079 Irudayaraj A.X.R. Wahab N.I.A. Veerasamy V. Premkumar M. Ramachandaramurthy V.K. Gooi H.B.
format	Conference Paper
author	Irudayaraj A.X.R. Wahab N.I.A. Veerasamy V. Premkumar M. Ramachandaramurthy V.K. Gooi H.B.
author_sort	Irudayaraj A.X.R.
title	Optimal Frequency Regulation in Multi-Microgrid Systems using Federated Learning
title_short	Optimal Frequency Regulation in Multi-Microgrid Systems using Federated Learning
title_full	Optimal Frequency Regulation in Multi-Microgrid Systems using Federated Learning
title_fullStr	Optimal Frequency Regulation in Multi-Microgrid Systems using Federated Learning
title_full_unstemmed	Optimal Frequency Regulation in Multi-Microgrid Systems using Federated Learning
title_sort	optimal frequency regulation in multi-microgrid systems using federated learning
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2024
_version_	1814061064939110400
score	13.222552

Optimal Frequency Regulation in Multi-Microgrid Systems using Federated Learning

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