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Distributed leader-follower based adaptive consensus control for networked microgrids

Networked microgrids (NMGs) provide a promising solution for accommodating various distributed energy resources (DERs) and enhance its performance. However, the coordinated operation of the system with integration of a large number of DERs is major challenge. Therefore, this article aims to provide...

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Main Authors: Kandasamy, Jeevitha, Ramachandran, Rajeswari, Veerasamy, Veerapandiyan, Irudayaraj, Andrew Xavier Raj
Format: Article
Published: Elsevier Ltd 2024
Online Access:http://psasir.upm.edu.my/id/eprint/105837/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173313879&doi=10.1016%2fj.apenergy.2023.122083&partnerID=40&md5=4da8dc2f2ef8f606dedc41956e9f2d51
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spelling my.upm.eprints.1058372024-05-08T14:30:45Z http://psasir.upm.edu.my/id/eprint/105837/ Distributed leader-follower based adaptive consensus control for networked microgrids Kandasamy, Jeevitha Ramachandran, Rajeswari Veerasamy, Veerapandiyan Irudayaraj, Andrew Xavier Raj Networked microgrids (NMGs) provide a promising solution for accommodating various distributed energy resources (DERs) and enhance its performance. However, the coordinated operation of the system with integration of a large number of DERs is major challenge. Therefore, this article aims to provide a distributed control strategy (DCS) based on a leader-follower framework for the coordination of multiple DERs in NMGs. A fuzzy optimized Recurrent Hopfield Neural Network (F-HNN) designed self-adaptive fractional order proportional integral derivative (FOPID) controller is proposed for distributed frequency control of NMGs. Initially, a Lyapunov-based objective function is derived for weight updation of the proposed network. The fuzzy approach is used to optimize the output of the HNN based on its gradients. The proposed F-HNN based DCS is implemented in MATLAB/Simulink and validated for frequency regulation of NMGs through hardware-in-the-loop simulation (HIL) using OPAL-RT. The results obtained are compared with the conventional FOPID HNN tuned and other classical controls. The self-adaptiveness of the controller is demonstrated for change in renewable power generation. Furthermore, the resiliency of the controller is tested with communication failures and, plug and play operation of MGs. The results obtained showed that the frequency of the NMG system is well regulated within the band of ±0.2 Hz. Also, the transient and steady state performance reveals that the proposed DCS is more significant than other techniques. Elsevier Ltd 2024-01 Article PeerReviewed Kandasamy, Jeevitha and Ramachandran, Rajeswari and Veerasamy, Veerapandiyan and Irudayaraj, Andrew Xavier Raj (2024) Distributed leader-follower based adaptive consensus control for networked microgrids. Applied Energy, 353 (pt.A). art. no. 122083. pp. 1-22. ISSN 0306-2619; ESSN: 1872-9118 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173313879&doi=10.1016%2fj.apenergy.2023.122083&partnerID=40&md5=4da8dc2f2ef8f606dedc41956e9f2d51 10.1016/j.apenergy.2023.122083
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Networked microgrids (NMGs) provide a promising solution for accommodating various distributed energy resources (DERs) and enhance its performance. However, the coordinated operation of the system with integration of a large number of DERs is major challenge. Therefore, this article aims to provide a distributed control strategy (DCS) based on a leader-follower framework for the coordination of multiple DERs in NMGs. A fuzzy optimized Recurrent Hopfield Neural Network (F-HNN) designed self-adaptive fractional order proportional integral derivative (FOPID) controller is proposed for distributed frequency control of NMGs. Initially, a Lyapunov-based objective function is derived for weight updation of the proposed network. The fuzzy approach is used to optimize the output of the HNN based on its gradients. The proposed F-HNN based DCS is implemented in MATLAB/Simulink and validated for frequency regulation of NMGs through hardware-in-the-loop simulation (HIL) using OPAL-RT. The results obtained are compared with the conventional FOPID HNN tuned and other classical controls. The self-adaptiveness of the controller is demonstrated for change in renewable power generation. Furthermore, the resiliency of the controller is tested with communication failures and, plug and play operation of MGs. The results obtained showed that the frequency of the NMG system is well regulated within the band of ±0.2 Hz. Also, the transient and steady state performance reveals that the proposed DCS is more significant than other techniques.
format Article
author Kandasamy, Jeevitha
Ramachandran, Rajeswari
Veerasamy, Veerapandiyan
Irudayaraj, Andrew Xavier Raj
spellingShingle Kandasamy, Jeevitha
Ramachandran, Rajeswari
Veerasamy, Veerapandiyan
Irudayaraj, Andrew Xavier Raj
Distributed leader-follower based adaptive consensus control for networked microgrids
author_facet Kandasamy, Jeevitha
Ramachandran, Rajeswari
Veerasamy, Veerapandiyan
Irudayaraj, Andrew Xavier Raj
author_sort Kandasamy, Jeevitha
title Distributed leader-follower based adaptive consensus control for networked microgrids
title_short Distributed leader-follower based adaptive consensus control for networked microgrids
title_full Distributed leader-follower based adaptive consensus control for networked microgrids
title_fullStr Distributed leader-follower based adaptive consensus control for networked microgrids
title_full_unstemmed Distributed leader-follower based adaptive consensus control for networked microgrids
title_sort distributed leader-follower based adaptive consensus control for networked microgrids
publisher Elsevier Ltd
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/105837/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173313879&doi=10.1016%2fj.apenergy.2023.122083&partnerID=40&md5=4da8dc2f2ef8f606dedc41956e9f2d51
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score 13.211869

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