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Supermodal Decomposition of the Linear Swing Equation for Multilayer Networks

We study the swing equation in the case of a multilayer network in which generators and motors are modeled differently; namely, the model for each generator is given by second order dynamics and the model for each motor is given by first order dynamics. We also remove the commonly used assumption of...

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Autores principales: BHATTA, KSHITIJ, NAZERIAN, AMIRHOSSEIN, SORRENTINO, FRANCESCO
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354730/
https://www.ncbi.nlm.nih.gov/pubmed/35937641
http://dx.doi.org/10.1109/access.2022.3188392
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author BHATTA, KSHITIJ
NAZERIAN, AMIRHOSSEIN
SORRENTINO, FRANCESCO
author_facet BHATTA, KSHITIJ
NAZERIAN, AMIRHOSSEIN
SORRENTINO, FRANCESCO
author_sort BHATTA, KSHITIJ
collection PubMed
description We study the swing equation in the case of a multilayer network in which generators and motors are modeled differently; namely, the model for each generator is given by second order dynamics and the model for each motor is given by first order dynamics. We also remove the commonly used assumption of equal damping coefficients in the second order dynamics. Under these general conditions, we are able to obtain a decomposition of the linear swing equation into independent modes describing the propagation of small perturbations. In the process, we identify symmetries affecting the structure and dynamics of the multilayer network and derive an essential model based on a ‘quotient network.’ We then compare the dynamics of the full network and that of the quotient network and obtain a modal decomposition of the error dynamics. We also provide a method to quantify the steady-state error and the maximum overshoot error. Two case studies are presented to illustrate application of our method.
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spelling pubmed-93547302022-08-05 Supermodal Decomposition of the Linear Swing Equation for Multilayer Networks BHATTA, KSHITIJ NAZERIAN, AMIRHOSSEIN SORRENTINO, FRANCESCO IEEE Access Article We study the swing equation in the case of a multilayer network in which generators and motors are modeled differently; namely, the model for each generator is given by second order dynamics and the model for each motor is given by first order dynamics. We also remove the commonly used assumption of equal damping coefficients in the second order dynamics. Under these general conditions, we are able to obtain a decomposition of the linear swing equation into independent modes describing the propagation of small perturbations. In the process, we identify symmetries affecting the structure and dynamics of the multilayer network and derive an essential model based on a ‘quotient network.’ We then compare the dynamics of the full network and that of the quotient network and obtain a modal decomposition of the error dynamics. We also provide a method to quantify the steady-state error and the maximum overshoot error. Two case studies are presented to illustrate application of our method. 2022 2022-07-04 /pmc/articles/PMC9354730/ /pubmed/35937641 http://dx.doi.org/10.1109/access.2022.3188392 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
spellingShingle Article
BHATTA, KSHITIJ
NAZERIAN, AMIRHOSSEIN
SORRENTINO, FRANCESCO
Supermodal Decomposition of the Linear Swing Equation for Multilayer Networks
title Supermodal Decomposition of the Linear Swing Equation for Multilayer Networks
title_full Supermodal Decomposition of the Linear Swing Equation for Multilayer Networks
title_fullStr Supermodal Decomposition of the Linear Swing Equation for Multilayer Networks
title_full_unstemmed Supermodal Decomposition of the Linear Swing Equation for Multilayer Networks
title_short Supermodal Decomposition of the Linear Swing Equation for Multilayer Networks
title_sort supermodal decomposition of the linear swing equation for multilayer networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354730/
https://www.ncbi.nlm.nih.gov/pubmed/35937641
http://dx.doi.org/10.1109/access.2022.3188392
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