Radial basis function for fast voltage stability assessment using Phasor Measurement Units

A simple method, based on Machine Learning Radial Basis Functions, RBF, is developed for estimating voltage stability margins in power systems. A reduced set of magnitude and angles of bus voltage phasors is used as input. Observability optimization technique for locating Phasor Measurement Units, P...
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Autor*in:	Jorge W. Gonzalez [verfasserIn] Idi A. Isaac [verfasserIn] Gabriel J. Lopez [verfasserIn] Hugo A. Cardona [verfasserIn] Gabriel J. Salazar [verfasserIn] John M. Rincon [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Electrical engineering Electrical system planning Power engineering Electric power transmission Power system operation Power system planning

Übergeordnetes Werk:	In: Heliyon - Elsevier, 2016, 5(2019), 11, Seite e02704-
Übergeordnetes Werk:	volume:5 ; year:2019 ; number:11 ; pages:e02704-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.heliyon.2019.e02704

Katalog-ID:	DOAJ067873707

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520			\|a A simple method, based on Machine Learning Radial Basis Functions, RBF, is developed for estimating voltage stability margins in power systems. A reduced set of magnitude and angles of bus voltage phasors is used as input. Observability optimization technique for locating Phasor Measurement Units, PMUs, is applied. A RBF is designed and used for fast calculation of voltage stability margins for online applications with PMUs. The method allows estimating active local and global power margins in normal operation and under contingencies. Optimized placement of PMUs leads to a minimum number of these devices to estimate the margins, but is shown that it is not a matter of PMUs quantity but of PMUs location for decreasing training time or having success in estimation convergence. Compared with previous work, the most significant enhancement is that our RBF learns from PMU data. To test the proposed method, validations in the IEEE 14-bus system and in a real electrical network are done.
650		4	\|a Electrical engineering
650		4	\|a Electrical system planning
650		4	\|a Power engineering
650		4	\|a Electric power transmission
650		4	\|a Power system operation
650		4	\|a Power system planning
653		0	\|a Science (General)
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700	0		\|a Gabriel J. Lopez \|e verfasserin \|4 aut
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700	0		\|a Gabriel J. Salazar \|e verfasserin \|4 aut
700	0		\|a John M. Rincon \|e verfasserin \|4 aut
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912			\|a GBV_ILN_4325
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publishDate	2019
allfields	10.1016/j.heliyon.2019.e02704 doi (DE-627)DOAJ067873707 (DE-599)DOAJ1b7c84dcb23b42dc859a9cab699c2d4f DE-627 ger DE-627 rakwb eng Q1-390 H1-99 Jorge W. Gonzalez verfasserin aut Radial basis function for fast voltage stability assessment using Phasor Measurement Units 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A simple method, based on Machine Learning Radial Basis Functions, RBF, is developed for estimating voltage stability margins in power systems. A reduced set of magnitude and angles of bus voltage phasors is used as input. Observability optimization technique for locating Phasor Measurement Units, PMUs, is applied. A RBF is designed and used for fast calculation of voltage stability margins for online applications with PMUs. The method allows estimating active local and global power margins in normal operation and under contingencies. Optimized placement of PMUs leads to a minimum number of these devices to estimate the margins, but is shown that it is not a matter of PMUs quantity but of PMUs location for decreasing training time or having success in estimation convergence. Compared with previous work, the most significant enhancement is that our RBF learns from PMU data. To test the proposed method, validations in the IEEE 14-bus system and in a real electrical network are done. Electrical engineering Electrical system planning Power engineering Electric power transmission Power system operation Power system planning Science (General) Social sciences (General) Idi A. Isaac verfasserin aut Gabriel J. Lopez verfasserin aut Hugo A. Cardona verfasserin aut Gabriel J. Salazar verfasserin aut John M. Rincon verfasserin aut In Heliyon Elsevier, 2016 5(2019), 11, Seite e02704- (DE-627)835893197 (DE-600)2835763-2 24058440 nnns volume:5 year:2019 number:11 pages:e02704- https://doi.org/10.1016/j.heliyon.2019.e02704 kostenfrei https://doaj.org/article/1b7c84dcb23b42dc859a9cab699c2d4f kostenfrei http://www.sciencedirect.com/science/article/pii/S2405844019363649 kostenfrei https://doaj.org/toc/2405-8440 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 5 2019 11 e02704-
spelling	10.1016/j.heliyon.2019.e02704 doi (DE-627)DOAJ067873707 (DE-599)DOAJ1b7c84dcb23b42dc859a9cab699c2d4f DE-627 ger DE-627 rakwb eng Q1-390 H1-99 Jorge W. Gonzalez verfasserin aut Radial basis function for fast voltage stability assessment using Phasor Measurement Units 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A simple method, based on Machine Learning Radial Basis Functions, RBF, is developed for estimating voltage stability margins in power systems. A reduced set of magnitude and angles of bus voltage phasors is used as input. Observability optimization technique for locating Phasor Measurement Units, PMUs, is applied. A RBF is designed and used for fast calculation of voltage stability margins for online applications with PMUs. The method allows estimating active local and global power margins in normal operation and under contingencies. Optimized placement of PMUs leads to a minimum number of these devices to estimate the margins, but is shown that it is not a matter of PMUs quantity but of PMUs location for decreasing training time or having success in estimation convergence. Compared with previous work, the most significant enhancement is that our RBF learns from PMU data. To test the proposed method, validations in the IEEE 14-bus system and in a real electrical network are done. Electrical engineering Electrical system planning Power engineering Electric power transmission Power system operation Power system planning Science (General) Social sciences (General) Idi A. Isaac verfasserin aut Gabriel J. Lopez verfasserin aut Hugo A. Cardona verfasserin aut Gabriel J. Salazar verfasserin aut John M. Rincon verfasserin aut In Heliyon Elsevier, 2016 5(2019), 11, Seite e02704- (DE-627)835893197 (DE-600)2835763-2 24058440 nnns volume:5 year:2019 number:11 pages:e02704- https://doi.org/10.1016/j.heliyon.2019.e02704 kostenfrei https://doaj.org/article/1b7c84dcb23b42dc859a9cab699c2d4f kostenfrei http://www.sciencedirect.com/science/article/pii/S2405844019363649 kostenfrei https://doaj.org/toc/2405-8440 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 5 2019 11 e02704-
allfields_unstemmed	10.1016/j.heliyon.2019.e02704 doi (DE-627)DOAJ067873707 (DE-599)DOAJ1b7c84dcb23b42dc859a9cab699c2d4f DE-627 ger DE-627 rakwb eng Q1-390 H1-99 Jorge W. Gonzalez verfasserin aut Radial basis function for fast voltage stability assessment using Phasor Measurement Units 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A simple method, based on Machine Learning Radial Basis Functions, RBF, is developed for estimating voltage stability margins in power systems. A reduced set of magnitude and angles of bus voltage phasors is used as input. Observability optimization technique for locating Phasor Measurement Units, PMUs, is applied. A RBF is designed and used for fast calculation of voltage stability margins for online applications with PMUs. The method allows estimating active local and global power margins in normal operation and under contingencies. Optimized placement of PMUs leads to a minimum number of these devices to estimate the margins, but is shown that it is not a matter of PMUs quantity but of PMUs location for decreasing training time or having success in estimation convergence. Compared with previous work, the most significant enhancement is that our RBF learns from PMU data. To test the proposed method, validations in the IEEE 14-bus system and in a real electrical network are done. Electrical engineering Electrical system planning Power engineering Electric power transmission Power system operation Power system planning Science (General) Social sciences (General) Idi A. Isaac verfasserin aut Gabriel J. Lopez verfasserin aut Hugo A. Cardona verfasserin aut Gabriel J. Salazar verfasserin aut John M. Rincon verfasserin aut In Heliyon Elsevier, 2016 5(2019), 11, Seite e02704- (DE-627)835893197 (DE-600)2835763-2 24058440 nnns volume:5 year:2019 number:11 pages:e02704- https://doi.org/10.1016/j.heliyon.2019.e02704 kostenfrei https://doaj.org/article/1b7c84dcb23b42dc859a9cab699c2d4f kostenfrei http://www.sciencedirect.com/science/article/pii/S2405844019363649 kostenfrei https://doaj.org/toc/2405-8440 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 5 2019 11 e02704-
allfieldsGer	10.1016/j.heliyon.2019.e02704 doi (DE-627)DOAJ067873707 (DE-599)DOAJ1b7c84dcb23b42dc859a9cab699c2d4f DE-627 ger DE-627 rakwb eng Q1-390 H1-99 Jorge W. Gonzalez verfasserin aut Radial basis function for fast voltage stability assessment using Phasor Measurement Units 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A simple method, based on Machine Learning Radial Basis Functions, RBF, is developed for estimating voltage stability margins in power systems. A reduced set of magnitude and angles of bus voltage phasors is used as input. Observability optimization technique for locating Phasor Measurement Units, PMUs, is applied. A RBF is designed and used for fast calculation of voltage stability margins for online applications with PMUs. The method allows estimating active local and global power margins in normal operation and under contingencies. Optimized placement of PMUs leads to a minimum number of these devices to estimate the margins, but is shown that it is not a matter of PMUs quantity but of PMUs location for decreasing training time or having success in estimation convergence. Compared with previous work, the most significant enhancement is that our RBF learns from PMU data. To test the proposed method, validations in the IEEE 14-bus system and in a real electrical network are done. Electrical engineering Electrical system planning Power engineering Electric power transmission Power system operation Power system planning Science (General) Social sciences (General) Idi A. Isaac verfasserin aut Gabriel J. Lopez verfasserin aut Hugo A. Cardona verfasserin aut Gabriel J. Salazar verfasserin aut John M. Rincon verfasserin aut In Heliyon Elsevier, 2016 5(2019), 11, Seite e02704- (DE-627)835893197 (DE-600)2835763-2 24058440 nnns volume:5 year:2019 number:11 pages:e02704- https://doi.org/10.1016/j.heliyon.2019.e02704 kostenfrei https://doaj.org/article/1b7c84dcb23b42dc859a9cab699c2d4f kostenfrei http://www.sciencedirect.com/science/article/pii/S2405844019363649 kostenfrei https://doaj.org/toc/2405-8440 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 5 2019 11 e02704-
allfieldsSound	10.1016/j.heliyon.2019.e02704 doi (DE-627)DOAJ067873707 (DE-599)DOAJ1b7c84dcb23b42dc859a9cab699c2d4f DE-627 ger DE-627 rakwb eng Q1-390 H1-99 Jorge W. Gonzalez verfasserin aut Radial basis function for fast voltage stability assessment using Phasor Measurement Units 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A simple method, based on Machine Learning Radial Basis Functions, RBF, is developed for estimating voltage stability margins in power systems. A reduced set of magnitude and angles of bus voltage phasors is used as input. Observability optimization technique for locating Phasor Measurement Units, PMUs, is applied. A RBF is designed and used for fast calculation of voltage stability margins for online applications with PMUs. The method allows estimating active local and global power margins in normal operation and under contingencies. Optimized placement of PMUs leads to a minimum number of these devices to estimate the margins, but is shown that it is not a matter of PMUs quantity but of PMUs location for decreasing training time or having success in estimation convergence. Compared with previous work, the most significant enhancement is that our RBF learns from PMU data. To test the proposed method, validations in the IEEE 14-bus system and in a real electrical network are done. Electrical engineering Electrical system planning Power engineering Electric power transmission Power system operation Power system planning Science (General) Social sciences (General) Idi A. Isaac verfasserin aut Gabriel J. Lopez verfasserin aut Hugo A. Cardona verfasserin aut Gabriel J. Salazar verfasserin aut John M. Rincon verfasserin aut In Heliyon Elsevier, 2016 5(2019), 11, Seite e02704- (DE-627)835893197 (DE-600)2835763-2 24058440 nnns volume:5 year:2019 number:11 pages:e02704- https://doi.org/10.1016/j.heliyon.2019.e02704 kostenfrei https://doaj.org/article/1b7c84dcb23b42dc859a9cab699c2d4f kostenfrei http://www.sciencedirect.com/science/article/pii/S2405844019363649 kostenfrei https://doaj.org/toc/2405-8440 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 5 2019 11 e02704-
language	English
source	In Heliyon 5(2019), 11, Seite e02704- volume:5 year:2019 number:11 pages:e02704-
sourceStr	In Heliyon 5(2019), 11, Seite e02704- volume:5 year:2019 number:11 pages:e02704-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Electrical engineering Electrical system planning Power engineering Electric power transmission Power system operation Power system planning Science (General) Social sciences (General)
isfreeaccess_bool	true
container_title	Heliyon
authorswithroles_txt_mv	Jorge W. Gonzalez @@aut@@ Idi A. Isaac @@aut@@ Gabriel J. Lopez @@aut@@ Hugo A. Cardona @@aut@@ Gabriel J. Salazar @@aut@@ John M. Rincon @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	835893197
id	DOAJ067873707
language_de	englisch
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callnumber-first	Q - Science
author	Jorge W. Gonzalez
spellingShingle	Jorge W. Gonzalez misc Q1-390 misc H1-99 misc Electrical engineering misc Electrical system planning misc Power engineering misc Electric power transmission misc Power system operation misc Power system planning misc Science (General) misc Social sciences (General) Radial basis function for fast voltage stability assessment using Phasor Measurement Units
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topic_title	Q1-390 H1-99 Radial basis function for fast voltage stability assessment using Phasor Measurement Units Electrical engineering Electrical system planning Power engineering Electric power transmission Power system operation Power system planning
topic	misc Q1-390 misc H1-99 misc Electrical engineering misc Electrical system planning misc Power engineering misc Electric power transmission misc Power system operation misc Power system planning misc Science (General) misc Social sciences (General)
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title	Radial basis function for fast voltage stability assessment using Phasor Measurement Units
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title_sort	radial basis function for fast voltage stability assessment using phasor measurement units
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title_auth	Radial basis function for fast voltage stability assessment using Phasor Measurement Units
abstract	A simple method, based on Machine Learning Radial Basis Functions, RBF, is developed for estimating voltage stability margins in power systems. A reduced set of magnitude and angles of bus voltage phasors is used as input. Observability optimization technique for locating Phasor Measurement Units, PMUs, is applied. A RBF is designed and used for fast calculation of voltage stability margins for online applications with PMUs. The method allows estimating active local and global power margins in normal operation and under contingencies. Optimized placement of PMUs leads to a minimum number of these devices to estimate the margins, but is shown that it is not a matter of PMUs quantity but of PMUs location for decreasing training time or having success in estimation convergence. Compared with previous work, the most significant enhancement is that our RBF learns from PMU data. To test the proposed method, validations in the IEEE 14-bus system and in a real electrical network are done.
abstractGer	A simple method, based on Machine Learning Radial Basis Functions, RBF, is developed for estimating voltage stability margins in power systems. A reduced set of magnitude and angles of bus voltage phasors is used as input. Observability optimization technique for locating Phasor Measurement Units, PMUs, is applied. A RBF is designed and used for fast calculation of voltage stability margins for online applications with PMUs. The method allows estimating active local and global power margins in normal operation and under contingencies. Optimized placement of PMUs leads to a minimum number of these devices to estimate the margins, but is shown that it is not a matter of PMUs quantity but of PMUs location for decreasing training time or having success in estimation convergence. Compared with previous work, the most significant enhancement is that our RBF learns from PMU data. To test the proposed method, validations in the IEEE 14-bus system and in a real electrical network are done.
abstract_unstemmed	A simple method, based on Machine Learning Radial Basis Functions, RBF, is developed for estimating voltage stability margins in power systems. A reduced set of magnitude and angles of bus voltage phasors is used as input. Observability optimization technique for locating Phasor Measurement Units, PMUs, is applied. A RBF is designed and used for fast calculation of voltage stability margins for online applications with PMUs. The method allows estimating active local and global power margins in normal operation and under contingencies. Optimized placement of PMUs leads to a minimum number of these devices to estimate the margins, but is shown that it is not a matter of PMUs quantity but of PMUs location for decreasing training time or having success in estimation convergence. Compared with previous work, the most significant enhancement is that our RBF learns from PMU data. To test the proposed method, validations in the IEEE 14-bus system and in a real electrical network are done.
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title_short	Radial basis function for fast voltage stability assessment using Phasor Measurement Units
url	https://doi.org/10.1016/j.heliyon.2019.e02704 https://doaj.org/article/1b7c84dcb23b42dc859a9cab699c2d4f http://www.sciencedirect.com/science/article/pii/S2405844019363649 https://doaj.org/toc/2405-8440
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Radial basis function for fast voltage stability assessment using Phasor Measurement Units

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