Transformer-based deep learning model for forced oscillation localization

Autores
Matar, Mustafa; Gill Estevez, Pablo Daniel; Marchi, Pablo Gabriel; Messina, Francisco Javier; Elmoudi, Ramadan; Wshah, Safwan
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Accurately locating Forced Oscillations (FOs) source(s) in a large-scale power system is a challenging task, and an important aspect of power system operation. In this paper, a complementary use of Deep Learning (DL)-based and Dissipating Energy Flow (DEF)-based methods are proposed to localize forced oscillation source(s) using data from Phasor Measurement Units (PMUs), by tracing the forced oscillations source(s) on the branch level in the power system network. The robustness, effectiveness and speed of the proposed approach is demonstrated in a WECC 240-bus test system, with high renewable integration in the system. Several simulated cases were tested, including non-gaussian noise, partially observable system, and operational topology variations in the system which correspond to real-world challenges. Timely localization of forced oscillation at an early stage provides the opportunity for taking remedial reaction. The results show that without the information of system operational topology, the proposed method can achieve high localization accuracy in only 0.33 s.
Fil: Matar, Mustafa. University Of Vermont.; Estados Unidos
Fil: Gill Estevez, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina
Fil: Marchi, Pablo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina
Fil: Messina, Francisco Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Ctro de Simulación Computacional P/aplicaciones Tecnologicas; Argentina
Fil: Elmoudi, Ramadan. No especifíca;
Fil: Wshah, Safwan. University Of Vermont.; Estados Unidos
Materia
DEEP LEARNING
DISSIPATING ENERGY
FORCED OSCILLATIONS
PHASOR MEASUREMENT UNIT (PMU)
TRANSFORMER-BASED DEEP LEARNING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/226670
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Transformer-based deep learning model for forced oscillation localizationMatar, MustafaGill Estevez, Pablo DanielMarchi, Pablo GabrielMessina, Francisco JavierElmoudi, RamadanWshah, SafwanDEEP LEARNINGDISSIPATING ENERGYFORCED OSCILLATIONSPHASOR MEASUREMENT UNIT (PMU)TRANSFORMER-BASED DEEP LEARNINGhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Accurately locating Forced Oscillations (FOs) source(s) in a large-scale power system is a challenging task, and an important aspect of power system operation. In this paper, a complementary use of Deep Learning (DL)-based and Dissipating Energy Flow (DEF)-based methods are proposed to localize forced oscillation source(s) using data from Phasor Measurement Units (PMUs), by tracing the forced oscillations source(s) on the branch level in the power system network. The robustness, effectiveness and speed of the proposed approach is demonstrated in a WECC 240-bus test system, with high renewable integration in the system. Several simulated cases were tested, including non-gaussian noise, partially observable system, and operational topology variations in the system which correspond to real-world challenges. Timely localization of forced oscillation at an early stage provides the opportunity for taking remedial reaction. The results show that without the information of system operational topology, the proposed method can achieve high localization accuracy in only 0.33 s.Fil: Matar, Mustafa. University Of Vermont.; Estados UnidosFil: Gill Estevez, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; ArgentinaFil: Marchi, Pablo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; ArgentinaFil: Messina, Francisco Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Ctro de Simulación Computacional P/aplicaciones Tecnologicas; ArgentinaFil: Elmoudi, Ramadan. No especifíca;Fil: Wshah, Safwan. University Of Vermont.; Estados UnidosElsevier2023-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/226670Matar, Mustafa; Gill Estevez, Pablo Daniel; Marchi, Pablo Gabriel; Messina, Francisco Javier; Elmoudi, Ramadan; et al.; Transformer-based deep learning model for forced oscillation localization; Elsevier; International Journal of Electrical Power & Energy Systems; 146; 3-2023; 1-110142-0615CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0142061522008018info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ijepes.2022.108805info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:12:16Zoai:ri.conicet.gov.ar:11336/226670instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:12:16.504CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Transformer-based deep learning model for forced oscillation localization
title Transformer-based deep learning model for forced oscillation localization
spellingShingle Transformer-based deep learning model for forced oscillation localization
Matar, Mustafa
DEEP LEARNING
DISSIPATING ENERGY
FORCED OSCILLATIONS
PHASOR MEASUREMENT UNIT (PMU)
TRANSFORMER-BASED DEEP LEARNING
title_short Transformer-based deep learning model for forced oscillation localization
title_full Transformer-based deep learning model for forced oscillation localization
title_fullStr Transformer-based deep learning model for forced oscillation localization
title_full_unstemmed Transformer-based deep learning model for forced oscillation localization
title_sort Transformer-based deep learning model for forced oscillation localization
dc.creator.none.fl_str_mv Matar, Mustafa
Gill Estevez, Pablo Daniel
Marchi, Pablo Gabriel
Messina, Francisco Javier
Elmoudi, Ramadan
Wshah, Safwan
author Matar, Mustafa
author_facet Matar, Mustafa
Gill Estevez, Pablo Daniel
Marchi, Pablo Gabriel
Messina, Francisco Javier
Elmoudi, Ramadan
Wshah, Safwan
author_role author
author2 Gill Estevez, Pablo Daniel
Marchi, Pablo Gabriel
Messina, Francisco Javier
Elmoudi, Ramadan
Wshah, Safwan
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv DEEP LEARNING
DISSIPATING ENERGY
FORCED OSCILLATIONS
PHASOR MEASUREMENT UNIT (PMU)
TRANSFORMER-BASED DEEP LEARNING
topic DEEP LEARNING
DISSIPATING ENERGY
FORCED OSCILLATIONS
PHASOR MEASUREMENT UNIT (PMU)
TRANSFORMER-BASED DEEP LEARNING
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Accurately locating Forced Oscillations (FOs) source(s) in a large-scale power system is a challenging task, and an important aspect of power system operation. In this paper, a complementary use of Deep Learning (DL)-based and Dissipating Energy Flow (DEF)-based methods are proposed to localize forced oscillation source(s) using data from Phasor Measurement Units (PMUs), by tracing the forced oscillations source(s) on the branch level in the power system network. The robustness, effectiveness and speed of the proposed approach is demonstrated in a WECC 240-bus test system, with high renewable integration in the system. Several simulated cases were tested, including non-gaussian noise, partially observable system, and operational topology variations in the system which correspond to real-world challenges. Timely localization of forced oscillation at an early stage provides the opportunity for taking remedial reaction. The results show that without the information of system operational topology, the proposed method can achieve high localization accuracy in only 0.33 s.
Fil: Matar, Mustafa. University Of Vermont.; Estados Unidos
Fil: Gill Estevez, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina
Fil: Marchi, Pablo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina
Fil: Messina, Francisco Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Ctro de Simulación Computacional P/aplicaciones Tecnologicas; Argentina
Fil: Elmoudi, Ramadan. No especifíca;
Fil: Wshah, Safwan. University Of Vermont.; Estados Unidos
description Accurately locating Forced Oscillations (FOs) source(s) in a large-scale power system is a challenging task, and an important aspect of power system operation. In this paper, a complementary use of Deep Learning (DL)-based and Dissipating Energy Flow (DEF)-based methods are proposed to localize forced oscillation source(s) using data from Phasor Measurement Units (PMUs), by tracing the forced oscillations source(s) on the branch level in the power system network. The robustness, effectiveness and speed of the proposed approach is demonstrated in a WECC 240-bus test system, with high renewable integration in the system. Several simulated cases were tested, including non-gaussian noise, partially observable system, and operational topology variations in the system which correspond to real-world challenges. Timely localization of forced oscillation at an early stage provides the opportunity for taking remedial reaction. The results show that without the information of system operational topology, the proposed method can achieve high localization accuracy in only 0.33 s.
publishDate 2023
dc.date.none.fl_str_mv 2023-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/226670
Matar, Mustafa; Gill Estevez, Pablo Daniel; Marchi, Pablo Gabriel; Messina, Francisco Javier; Elmoudi, Ramadan; et al.; Transformer-based deep learning model for forced oscillation localization; Elsevier; International Journal of Electrical Power & Energy Systems; 146; 3-2023; 1-11
0142-0615
CONICET Digital
CONICET
url http://hdl.handle.net/11336/226670
identifier_str_mv Matar, Mustafa; Gill Estevez, Pablo Daniel; Marchi, Pablo Gabriel; Messina, Francisco Javier; Elmoudi, Ramadan; et al.; Transformer-based deep learning model for forced oscillation localization; Elsevier; International Journal of Electrical Power & Energy Systems; 146; 3-2023; 1-11
0142-0615
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0142061522008018
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ijepes.2022.108805
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846083270859554816
score 13.22299

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