Long-term voltage stability monitoring of power system areas using a kernel extreme learning machine approach

The ever increasing active and reactive power demands, along with limited sources of generation and delays in transmission expansion projects, have led many power systems to operate near their voltage stability limits. In this context, voltage stability monitoring methodologies have become an import...

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Autores principales:	Walter M. Villa-Acevedo, Jesús M. López-Lezama, Delia G. Colomé, Jaime Cepeda
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Angle cut-set Kernel extreme learning machine (KELM) Long-term voltage stability monitoring Near real-time monitoring Voltage control area Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/31e707ccc5a64c85b238e74304b742b0
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id	oai:doaj.org-article:31e707ccc5a64c85b238e74304b742b0
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spelling	oai:doaj.org-article:31e707ccc5a64c85b238e74304b742b02021-11-18T04:45:10ZLong-term voltage stability monitoring of power system areas using a kernel extreme learning machine approach1110-016810.1016/j.aej.2021.06.013https://doaj.org/article/31e707ccc5a64c85b238e74304b742b02022-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1110016821003781https://doaj.org/toc/1110-0168The ever increasing active and reactive power demands, along with limited sources of generation and delays in transmission expansion projects, have led many power systems to operate near their voltage stability limits. In this context, voltage stability monitoring methodologies have become an important topic in power systems research. This paper presents a novel methodology for long-term voltage stability monitoring in power systems that exploits the feasibility of phasor-type information in order to estimate the long-term voltage stability status. The information regarding the current system condition is acquired through synchronized phasor measurements and the power system is divided in sub-areas for improving its supervision; then, an artificial intelligence approach based on kernel extreme learning machine is used for long-term voltage stability assessment. The proposed scheme allows foreseeing the voltage instability caused by limitations in reactive power transmission, and it also permits alerting when a system area experiences a deficit of reactive power from supply sources. The validation of the proposed method is performed on the 39-bus test system, obtaining feasible results. The tests confirmed that the proposed method works properly under different scenarios and system conditions, always ensuring proper voltage stability status results independently of its cause.Walter M. Villa-AcevedoJesús M. López-LezamaDelia G. ColoméJaime CepedaElsevierarticleAngle cut-setKernel extreme learning machine (KELM)Long-term voltage stability monitoringNear real-time monitoringVoltage control areaEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 2, Pp 1353-1367 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Angle cut-set Kernel extreme learning machine (KELM) Long-term voltage stability monitoring Near real-time monitoring Voltage control area Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Angle cut-set Kernel extreme learning machine (KELM) Long-term voltage stability monitoring Near real-time monitoring Voltage control area Engineering (General). Civil engineering (General) TA1-2040 Walter M. Villa-Acevedo Jesús M. López-Lezama Delia G. Colomé Jaime Cepeda Long-term voltage stability monitoring of power system areas using a kernel extreme learning machine approach
description	The ever increasing active and reactive power demands, along with limited sources of generation and delays in transmission expansion projects, have led many power systems to operate near their voltage stability limits. In this context, voltage stability monitoring methodologies have become an important topic in power systems research. This paper presents a novel methodology for long-term voltage stability monitoring in power systems that exploits the feasibility of phasor-type information in order to estimate the long-term voltage stability status. The information regarding the current system condition is acquired through synchronized phasor measurements and the power system is divided in sub-areas for improving its supervision; then, an artificial intelligence approach based on kernel extreme learning machine is used for long-term voltage stability assessment. The proposed scheme allows foreseeing the voltage instability caused by limitations in reactive power transmission, and it also permits alerting when a system area experiences a deficit of reactive power from supply sources. The validation of the proposed method is performed on the 39-bus test system, obtaining feasible results. The tests confirmed that the proposed method works properly under different scenarios and system conditions, always ensuring proper voltage stability status results independently of its cause.
format	article
author	Walter M. Villa-Acevedo Jesús M. López-Lezama Delia G. Colomé Jaime Cepeda
author_facet	Walter M. Villa-Acevedo Jesús M. López-Lezama Delia G. Colomé Jaime Cepeda
author_sort	Walter M. Villa-Acevedo
title	Long-term voltage stability monitoring of power system areas using a kernel extreme learning machine approach
title_short	Long-term voltage stability monitoring of power system areas using a kernel extreme learning machine approach
title_full	Long-term voltage stability monitoring of power system areas using a kernel extreme learning machine approach
title_fullStr	Long-term voltage stability monitoring of power system areas using a kernel extreme learning machine approach
title_full_unstemmed	Long-term voltage stability monitoring of power system areas using a kernel extreme learning machine approach
title_sort	long-term voltage stability monitoring of power system areas using a kernel extreme learning machine approach
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/31e707ccc5a64c85b238e74304b742b0
work_keys_str_mv	AT waltermvillaacevedo longtermvoltagestabilitymonitoringofpowersystemareasusingakernelextremelearningmachineapproach AT jesusmlopezlezama longtermvoltagestabilitymonitoringofpowersystemareasusingakernelextremelearningmachineapproach AT deliagcolome longtermvoltagestabilitymonitoringofpowersystemareasusingakernelextremelearningmachineapproach AT jaimecepeda longtermvoltagestabilitymonitoringofpowersystemareasusingakernelextremelearningmachineapproach
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Long-term voltage stability monitoring of power system areas using a kernel extreme learning machine approach

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