Subsynchronous oscillation analysis method based on broad learning system

This paper presents a method to analyze the Sub Synchronous Oscillation (SSO) problem based on broad learning system (BLS), which can quickly analyze the risk of SSO in a system and take measures. The simulation system is constructed by the eigenvalue analysis method to obtain training data, that so...
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Autor*in:	Ling Li [verfasserIn] Yang Mao [verfasserIn] Zhencheng Liang [verfasserIn] Shaozhe Li [verfasserIn] Cuiyun Luo [verfasserIn] Yangtian Ning [verfasserIn] Li Xiong [verfasserIn] Yude Yang [verfasserIn] Bin Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Subsynchronous oscillations Eigenvalue analysis Broad learning system Feature importance

Übergeordnetes Werk:	In: Energy Reports - Elsevier, 2016, 9(2023), Seite 221-234
Übergeordnetes Werk:	volume:9 ; year:2023 ; pages:221-234

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.egyr.2023.04.066

Katalog-ID:	DOAJ08958032X

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245	1	0	\|a Subsynchronous oscillation analysis method based on broad learning system
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520			\|a This paper presents a method to analyze the Sub Synchronous Oscillation (SSO) problem based on broad learning system (BLS), which can quickly analyze the risk of SSO in a system and take measures. The simulation system is constructed by the eigenvalue analysis method to obtain training data, that solves the problem which is difficult to obtain actual data in SSO problem and makes the model more accurate and the conclusion more convincing. Subsequently, BLS is applied for feature extraction and the prediction model is constructed, the model can show the deep relationship between the oscillation source and topology of the system as well as various operating data, which is difficult to be expressed by formula. The system was tested and validated in the 41-bus system. The feature importance analysis method which comes from eXtreme Gradient Boosting (XGboost) algorithm is proposed and combined with the BLS prediction model, which can analyze the influence of each variable on the prediction results in different series compensation levels, and is validated in the 41-bus system, that makes the results of prediction more interpretable. Finally, the results of simulation show that the model can predict data with high accuracy and give adjustment plans for effective control of the system to avoid the risk of SSO.
650		4	\|a Subsynchronous oscillations
650		4	\|a Eigenvalue analysis
650		4	\|a Broad learning system
650		4	\|a Feature importance
653		0	\|a Electrical engineering. Electronics. Nuclear engineering
700	0		\|a Yang Mao \|e verfasserin \|4 aut
700	0		\|a Zhencheng Liang \|e verfasserin \|4 aut
700	0		\|a Shaozhe Li \|e verfasserin \|4 aut
700	0		\|a Cuiyun Luo \|e verfasserin \|4 aut
700	0		\|a Yangtian Ning \|e verfasserin \|4 aut
700	0		\|a Li Xiong \|e verfasserin \|4 aut
700	0		\|a Yude Yang \|e verfasserin \|4 aut
700	0		\|a Bin Li \|e verfasserin \|4 aut
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author_variant	l l ll y m ym z l zl s l sl c l cl y n yn l x lx y y yy b l bl
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publishDate	2023
allfields	10.1016/j.egyr.2023.04.066 doi (DE-627)DOAJ08958032X (DE-599)DOAJ62c27ed333c94bc0aace26ba1c1aa99e DE-627 ger DE-627 rakwb eng TK1-9971 Ling Li verfasserin aut Subsynchronous oscillation analysis method based on broad learning system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a method to analyze the Sub Synchronous Oscillation (SSO) problem based on broad learning system (BLS), which can quickly analyze the risk of SSO in a system and take measures. The simulation system is constructed by the eigenvalue analysis method to obtain training data, that solves the problem which is difficult to obtain actual data in SSO problem and makes the model more accurate and the conclusion more convincing. Subsequently, BLS is applied for feature extraction and the prediction model is constructed, the model can show the deep relationship between the oscillation source and topology of the system as well as various operating data, which is difficult to be expressed by formula. The system was tested and validated in the 41-bus system. The feature importance analysis method which comes from eXtreme Gradient Boosting (XGboost) algorithm is proposed and combined with the BLS prediction model, which can analyze the influence of each variable on the prediction results in different series compensation levels, and is validated in the 41-bus system, that makes the results of prediction more interpretable. Finally, the results of simulation show that the model can predict data with high accuracy and give adjustment plans for effective control of the system to avoid the risk of SSO. Subsynchronous oscillations Eigenvalue analysis Broad learning system Feature importance Electrical engineering. Electronics. Nuclear engineering Yang Mao verfasserin aut Zhencheng Liang verfasserin aut Shaozhe Li verfasserin aut Cuiyun Luo verfasserin aut Yangtian Ning verfasserin aut Li Xiong verfasserin aut Yude Yang verfasserin aut Bin Li verfasserin aut In Energy Reports Elsevier, 2016 9(2023), Seite 221-234 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:9 year:2023 pages:221-234 https://doi.org/10.1016/j.egyr.2023.04.066 kostenfrei https://doaj.org/article/62c27ed333c94bc0aace26ba1c1aa99e kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723004298 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 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 9 2023 221-234
spelling	10.1016/j.egyr.2023.04.066 doi (DE-627)DOAJ08958032X (DE-599)DOAJ62c27ed333c94bc0aace26ba1c1aa99e DE-627 ger DE-627 rakwb eng TK1-9971 Ling Li verfasserin aut Subsynchronous oscillation analysis method based on broad learning system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a method to analyze the Sub Synchronous Oscillation (SSO) problem based on broad learning system (BLS), which can quickly analyze the risk of SSO in a system and take measures. The simulation system is constructed by the eigenvalue analysis method to obtain training data, that solves the problem which is difficult to obtain actual data in SSO problem and makes the model more accurate and the conclusion more convincing. Subsequently, BLS is applied for feature extraction and the prediction model is constructed, the model can show the deep relationship between the oscillation source and topology of the system as well as various operating data, which is difficult to be expressed by formula. The system was tested and validated in the 41-bus system. The feature importance analysis method which comes from eXtreme Gradient Boosting (XGboost) algorithm is proposed and combined with the BLS prediction model, which can analyze the influence of each variable on the prediction results in different series compensation levels, and is validated in the 41-bus system, that makes the results of prediction more interpretable. Finally, the results of simulation show that the model can predict data with high accuracy and give adjustment plans for effective control of the system to avoid the risk of SSO. Subsynchronous oscillations Eigenvalue analysis Broad learning system Feature importance Electrical engineering. Electronics. Nuclear engineering Yang Mao verfasserin aut Zhencheng Liang verfasserin aut Shaozhe Li verfasserin aut Cuiyun Luo verfasserin aut Yangtian Ning verfasserin aut Li Xiong verfasserin aut Yude Yang verfasserin aut Bin Li verfasserin aut In Energy Reports Elsevier, 2016 9(2023), Seite 221-234 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:9 year:2023 pages:221-234 https://doi.org/10.1016/j.egyr.2023.04.066 kostenfrei https://doaj.org/article/62c27ed333c94bc0aace26ba1c1aa99e kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723004298 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 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 9 2023 221-234
allfields_unstemmed	10.1016/j.egyr.2023.04.066 doi (DE-627)DOAJ08958032X (DE-599)DOAJ62c27ed333c94bc0aace26ba1c1aa99e DE-627 ger DE-627 rakwb eng TK1-9971 Ling Li verfasserin aut Subsynchronous oscillation analysis method based on broad learning system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a method to analyze the Sub Synchronous Oscillation (SSO) problem based on broad learning system (BLS), which can quickly analyze the risk of SSO in a system and take measures. The simulation system is constructed by the eigenvalue analysis method to obtain training data, that solves the problem which is difficult to obtain actual data in SSO problem and makes the model more accurate and the conclusion more convincing. Subsequently, BLS is applied for feature extraction and the prediction model is constructed, the model can show the deep relationship between the oscillation source and topology of the system as well as various operating data, which is difficult to be expressed by formula. The system was tested and validated in the 41-bus system. The feature importance analysis method which comes from eXtreme Gradient Boosting (XGboost) algorithm is proposed and combined with the BLS prediction model, which can analyze the influence of each variable on the prediction results in different series compensation levels, and is validated in the 41-bus system, that makes the results of prediction more interpretable. Finally, the results of simulation show that the model can predict data with high accuracy and give adjustment plans for effective control of the system to avoid the risk of SSO. Subsynchronous oscillations Eigenvalue analysis Broad learning system Feature importance Electrical engineering. Electronics. Nuclear engineering Yang Mao verfasserin aut Zhencheng Liang verfasserin aut Shaozhe Li verfasserin aut Cuiyun Luo verfasserin aut Yangtian Ning verfasserin aut Li Xiong verfasserin aut Yude Yang verfasserin aut Bin Li verfasserin aut In Energy Reports Elsevier, 2016 9(2023), Seite 221-234 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:9 year:2023 pages:221-234 https://doi.org/10.1016/j.egyr.2023.04.066 kostenfrei https://doaj.org/article/62c27ed333c94bc0aace26ba1c1aa99e kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723004298 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 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 9 2023 221-234
allfieldsGer	10.1016/j.egyr.2023.04.066 doi (DE-627)DOAJ08958032X (DE-599)DOAJ62c27ed333c94bc0aace26ba1c1aa99e DE-627 ger DE-627 rakwb eng TK1-9971 Ling Li verfasserin aut Subsynchronous oscillation analysis method based on broad learning system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a method to analyze the Sub Synchronous Oscillation (SSO) problem based on broad learning system (BLS), which can quickly analyze the risk of SSO in a system and take measures. The simulation system is constructed by the eigenvalue analysis method to obtain training data, that solves the problem which is difficult to obtain actual data in SSO problem and makes the model more accurate and the conclusion more convincing. Subsequently, BLS is applied for feature extraction and the prediction model is constructed, the model can show the deep relationship between the oscillation source and topology of the system as well as various operating data, which is difficult to be expressed by formula. The system was tested and validated in the 41-bus system. The feature importance analysis method which comes from eXtreme Gradient Boosting (XGboost) algorithm is proposed and combined with the BLS prediction model, which can analyze the influence of each variable on the prediction results in different series compensation levels, and is validated in the 41-bus system, that makes the results of prediction more interpretable. Finally, the results of simulation show that the model can predict data with high accuracy and give adjustment plans for effective control of the system to avoid the risk of SSO. Subsynchronous oscillations Eigenvalue analysis Broad learning system Feature importance Electrical engineering. Electronics. Nuclear engineering Yang Mao verfasserin aut Zhencheng Liang verfasserin aut Shaozhe Li verfasserin aut Cuiyun Luo verfasserin aut Yangtian Ning verfasserin aut Li Xiong verfasserin aut Yude Yang verfasserin aut Bin Li verfasserin aut In Energy Reports Elsevier, 2016 9(2023), Seite 221-234 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:9 year:2023 pages:221-234 https://doi.org/10.1016/j.egyr.2023.04.066 kostenfrei https://doaj.org/article/62c27ed333c94bc0aace26ba1c1aa99e kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723004298 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 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 9 2023 221-234
allfieldsSound	10.1016/j.egyr.2023.04.066 doi (DE-627)DOAJ08958032X (DE-599)DOAJ62c27ed333c94bc0aace26ba1c1aa99e DE-627 ger DE-627 rakwb eng TK1-9971 Ling Li verfasserin aut Subsynchronous oscillation analysis method based on broad learning system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a method to analyze the Sub Synchronous Oscillation (SSO) problem based on broad learning system (BLS), which can quickly analyze the risk of SSO in a system and take measures. The simulation system is constructed by the eigenvalue analysis method to obtain training data, that solves the problem which is difficult to obtain actual data in SSO problem and makes the model more accurate and the conclusion more convincing. Subsequently, BLS is applied for feature extraction and the prediction model is constructed, the model can show the deep relationship between the oscillation source and topology of the system as well as various operating data, which is difficult to be expressed by formula. The system was tested and validated in the 41-bus system. The feature importance analysis method which comes from eXtreme Gradient Boosting (XGboost) algorithm is proposed and combined with the BLS prediction model, which can analyze the influence of each variable on the prediction results in different series compensation levels, and is validated in the 41-bus system, that makes the results of prediction more interpretable. Finally, the results of simulation show that the model can predict data with high accuracy and give adjustment plans for effective control of the system to avoid the risk of SSO. Subsynchronous oscillations Eigenvalue analysis Broad learning system Feature importance Electrical engineering. Electronics. Nuclear engineering Yang Mao verfasserin aut Zhencheng Liang verfasserin aut Shaozhe Li verfasserin aut Cuiyun Luo verfasserin aut Yangtian Ning verfasserin aut Li Xiong verfasserin aut Yude Yang verfasserin aut Bin Li verfasserin aut In Energy Reports Elsevier, 2016 9(2023), Seite 221-234 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:9 year:2023 pages:221-234 https://doi.org/10.1016/j.egyr.2023.04.066 kostenfrei https://doaj.org/article/62c27ed333c94bc0aace26ba1c1aa99e kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723004298 kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 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 9 2023 221-234
language	English
source	In Energy Reports 9(2023), Seite 221-234 volume:9 year:2023 pages:221-234
sourceStr	In Energy Reports 9(2023), Seite 221-234 volume:9 year:2023 pages:221-234
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Subsynchronous oscillations Eigenvalue analysis Broad learning system Feature importance Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	Energy Reports
authorswithroles_txt_mv	Ling Li @@aut@@ Yang Mao @@aut@@ Zhencheng Liang @@aut@@ Shaozhe Li @@aut@@ Cuiyun Luo @@aut@@ Yangtian Ning @@aut@@ Li Xiong @@aut@@ Yude Yang @@aut@@ Bin Li @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	820689033
id	DOAJ08958032X
language_de	englisch
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callnumber-first	T - Technology
author	Ling Li
spellingShingle	Ling Li misc TK1-9971 misc Subsynchronous oscillations misc Eigenvalue analysis misc Broad learning system misc Feature importance misc Electrical engineering. Electronics. Nuclear engineering Subsynchronous oscillation analysis method based on broad learning system
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topic_title	TK1-9971 Subsynchronous oscillation analysis method based on broad learning system Subsynchronous oscillations Eigenvalue analysis Broad learning system Feature importance
topic	misc TK1-9971 misc Subsynchronous oscillations misc Eigenvalue analysis misc Broad learning system misc Feature importance misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Subsynchronous oscillations misc Eigenvalue analysis misc Broad learning system misc Feature importance misc Electrical engineering. Electronics. Nuclear engineering
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title	Subsynchronous oscillation analysis method based on broad learning system
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title_full	Subsynchronous oscillation analysis method based on broad learning system
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author_browse	Ling Li Yang Mao Zhencheng Liang Shaozhe Li Cuiyun Luo Yangtian Ning Li Xiong Yude Yang Bin Li
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title_sort	subsynchronous oscillation analysis method based on broad learning system
callnumber	TK1-9971
title_auth	Subsynchronous oscillation analysis method based on broad learning system
abstract	This paper presents a method to analyze the Sub Synchronous Oscillation (SSO) problem based on broad learning system (BLS), which can quickly analyze the risk of SSO in a system and take measures. The simulation system is constructed by the eigenvalue analysis method to obtain training data, that solves the problem which is difficult to obtain actual data in SSO problem and makes the model more accurate and the conclusion more convincing. Subsequently, BLS is applied for feature extraction and the prediction model is constructed, the model can show the deep relationship between the oscillation source and topology of the system as well as various operating data, which is difficult to be expressed by formula. The system was tested and validated in the 41-bus system. The feature importance analysis method which comes from eXtreme Gradient Boosting (XGboost) algorithm is proposed and combined with the BLS prediction model, which can analyze the influence of each variable on the prediction results in different series compensation levels, and is validated in the 41-bus system, that makes the results of prediction more interpretable. Finally, the results of simulation show that the model can predict data with high accuracy and give adjustment plans for effective control of the system to avoid the risk of SSO.
abstractGer	This paper presents a method to analyze the Sub Synchronous Oscillation (SSO) problem based on broad learning system (BLS), which can quickly analyze the risk of SSO in a system and take measures. The simulation system is constructed by the eigenvalue analysis method to obtain training data, that solves the problem which is difficult to obtain actual data in SSO problem and makes the model more accurate and the conclusion more convincing. Subsequently, BLS is applied for feature extraction and the prediction model is constructed, the model can show the deep relationship between the oscillation source and topology of the system as well as various operating data, which is difficult to be expressed by formula. The system was tested and validated in the 41-bus system. The feature importance analysis method which comes from eXtreme Gradient Boosting (XGboost) algorithm is proposed and combined with the BLS prediction model, which can analyze the influence of each variable on the prediction results in different series compensation levels, and is validated in the 41-bus system, that makes the results of prediction more interpretable. Finally, the results of simulation show that the model can predict data with high accuracy and give adjustment plans for effective control of the system to avoid the risk of SSO.
abstract_unstemmed	This paper presents a method to analyze the Sub Synchronous Oscillation (SSO) problem based on broad learning system (BLS), which can quickly analyze the risk of SSO in a system and take measures. The simulation system is constructed by the eigenvalue analysis method to obtain training data, that solves the problem which is difficult to obtain actual data in SSO problem and makes the model more accurate and the conclusion more convincing. Subsequently, BLS is applied for feature extraction and the prediction model is constructed, the model can show the deep relationship between the oscillation source and topology of the system as well as various operating data, which is difficult to be expressed by formula. The system was tested and validated in the 41-bus system. The feature importance analysis method which comes from eXtreme Gradient Boosting (XGboost) algorithm is proposed and combined with the BLS prediction model, which can analyze the influence of each variable on the prediction results in different series compensation levels, and is validated in the 41-bus system, that makes the results of prediction more interpretable. Finally, the results of simulation show that the model can predict data with high accuracy and give adjustment plans for effective control of the system to avoid the risk of SSO.
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title_short	Subsynchronous oscillation analysis method based on broad learning system
url	https://doi.org/10.1016/j.egyr.2023.04.066 https://doaj.org/article/62c27ed333c94bc0aace26ba1c1aa99e http://www.sciencedirect.com/science/article/pii/S2352484723004298 https://doaj.org/toc/2352-4847
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author2	Yang Mao Zhencheng Liang Shaozhe Li Cuiyun Luo Yangtian Ning Li Xiong Yude Yang Bin Li
author2Str	Yang Mao Zhencheng Liang Shaozhe Li Cuiyun Luo Yangtian Ning Li Xiong Yude Yang Bin Li
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up_date	2024-07-03T23:46:28.025Z
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The simulation system is constructed by the eigenvalue analysis method to obtain training data, that solves the problem which is difficult to obtain actual data in SSO problem and makes the model more accurate and the conclusion more convincing. Subsequently, BLS is applied for feature extraction and the prediction model is constructed, the model can show the deep relationship between the oscillation source and topology of the system as well as various operating data, which is difficult to be expressed by formula. The system was tested and validated in the 41-bus system. The feature importance analysis method which comes from eXtreme Gradient Boosting (XGboost) algorithm is proposed and combined with the BLS prediction model, which can analyze the influence of each variable on the prediction results in different series compensation levels, and is validated in the 41-bus system, that makes the results of prediction more interpretable. 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Subsynchronous oscillation analysis method based on broad learning system

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