An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm

Abstract The frequency response model of a microgrid system is an indispensable tool for designing secondary frequency controllers and analyzing system frequency stability. Owing to the complexity of a microgrid system structure, time‐varying operation state, and difficulty in obtaining the inverter...
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Autor*in:	Shi Yong [verfasserIn] Cheng Yin [verfasserIn] Su Jianhui [verfasserIn] Mao Meiqin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	butterfly algorithm microgrid online identification particle swarm optimization

Übergeordnetes Werk:	In: Energy Science & Engineering - Wiley, 2014, 11(2023), 5, Seite 1619-1629
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:5 ; pages:1619-1629

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1002/ese3.1407

Katalog-ID:	DOAJ090454391

Internformat


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520			\|a Abstract The frequency response model of a microgrid system is an indispensable tool for designing secondary frequency controllers and analyzing system frequency stability. Owing to the complexity of a microgrid system structure, time‐varying operation state, and difficulty in obtaining the inverter parameters, it is difficult to model the microgrid system. To address the abovementioned problems, this study constructs the equivalent model of a microgrid system based on mechanism analysis and then identifies the equivalent model parameters using an online identification method based on a hybrid particle swarm optimization butterfly algorithm according to the sampled operation data to obtain the specific equivalent model of a microgrid for the controller. This modeling method has strong adaptability and can yield accurate identification modeling when the operational structure of the microgrid changes.
650		4	\|a butterfly algorithm
650		4	\|a microgrid
650		4	\|a online identification
650		4	\|a particle swarm optimization
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912			\|a GBV_ILN_4336
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912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 11 \|j 2023 \|e 5 \|h 1619-1629

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publishDate	2023
allfields	10.1002/ese3.1407 doi (DE-627)DOAJ090454391 (DE-599)DOAJ38f38a8094cb4d4c9a95aad722767987 DE-627 ger DE-627 rakwb eng Shi Yong verfasserin aut An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The frequency response model of a microgrid system is an indispensable tool for designing secondary frequency controllers and analyzing system frequency stability. Owing to the complexity of a microgrid system structure, time‐varying operation state, and difficulty in obtaining the inverter parameters, it is difficult to model the microgrid system. To address the abovementioned problems, this study constructs the equivalent model of a microgrid system based on mechanism analysis and then identifies the equivalent model parameters using an online identification method based on a hybrid particle swarm optimization butterfly algorithm according to the sampled operation data to obtain the specific equivalent model of a microgrid for the controller. This modeling method has strong adaptability and can yield accurate identification modeling when the operational structure of the microgrid changes. butterfly algorithm microgrid online identification particle swarm optimization Technology T Science Q Cheng Yin verfasserin aut Su Jianhui verfasserin aut Mao Meiqin verfasserin aut In Energy Science & Engineering Wiley, 2014 11(2023), 5, Seite 1619-1629 (DE-627)750089202 (DE-600)2720339-6 20500505 nnns volume:11 year:2023 number:5 pages:1619-1629 https://doi.org/10.1002/ese3.1407 kostenfrei https://doaj.org/article/38f38a8094cb4d4c9a95aad722767987 kostenfrei https://doi.org/10.1002/ese3.1407 kostenfrei https://doaj.org/toc/2050-0505 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 5 1619-1629
spelling	10.1002/ese3.1407 doi (DE-627)DOAJ090454391 (DE-599)DOAJ38f38a8094cb4d4c9a95aad722767987 DE-627 ger DE-627 rakwb eng Shi Yong verfasserin aut An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The frequency response model of a microgrid system is an indispensable tool for designing secondary frequency controllers and analyzing system frequency stability. Owing to the complexity of a microgrid system structure, time‐varying operation state, and difficulty in obtaining the inverter parameters, it is difficult to model the microgrid system. To address the abovementioned problems, this study constructs the equivalent model of a microgrid system based on mechanism analysis and then identifies the equivalent model parameters using an online identification method based on a hybrid particle swarm optimization butterfly algorithm according to the sampled operation data to obtain the specific equivalent model of a microgrid for the controller. This modeling method has strong adaptability and can yield accurate identification modeling when the operational structure of the microgrid changes. butterfly algorithm microgrid online identification particle swarm optimization Technology T Science Q Cheng Yin verfasserin aut Su Jianhui verfasserin aut Mao Meiqin verfasserin aut In Energy Science & Engineering Wiley, 2014 11(2023), 5, Seite 1619-1629 (DE-627)750089202 (DE-600)2720339-6 20500505 nnns volume:11 year:2023 number:5 pages:1619-1629 https://doi.org/10.1002/ese3.1407 kostenfrei https://doaj.org/article/38f38a8094cb4d4c9a95aad722767987 kostenfrei https://doi.org/10.1002/ese3.1407 kostenfrei https://doaj.org/toc/2050-0505 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 5 1619-1629
allfields_unstemmed	10.1002/ese3.1407 doi (DE-627)DOAJ090454391 (DE-599)DOAJ38f38a8094cb4d4c9a95aad722767987 DE-627 ger DE-627 rakwb eng Shi Yong verfasserin aut An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The frequency response model of a microgrid system is an indispensable tool for designing secondary frequency controllers and analyzing system frequency stability. Owing to the complexity of a microgrid system structure, time‐varying operation state, and difficulty in obtaining the inverter parameters, it is difficult to model the microgrid system. To address the abovementioned problems, this study constructs the equivalent model of a microgrid system based on mechanism analysis and then identifies the equivalent model parameters using an online identification method based on a hybrid particle swarm optimization butterfly algorithm according to the sampled operation data to obtain the specific equivalent model of a microgrid for the controller. This modeling method has strong adaptability and can yield accurate identification modeling when the operational structure of the microgrid changes. butterfly algorithm microgrid online identification particle swarm optimization Technology T Science Q Cheng Yin verfasserin aut Su Jianhui verfasserin aut Mao Meiqin verfasserin aut In Energy Science & Engineering Wiley, 2014 11(2023), 5, Seite 1619-1629 (DE-627)750089202 (DE-600)2720339-6 20500505 nnns volume:11 year:2023 number:5 pages:1619-1629 https://doi.org/10.1002/ese3.1407 kostenfrei https://doaj.org/article/38f38a8094cb4d4c9a95aad722767987 kostenfrei https://doi.org/10.1002/ese3.1407 kostenfrei https://doaj.org/toc/2050-0505 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 5 1619-1629
allfieldsGer	10.1002/ese3.1407 doi (DE-627)DOAJ090454391 (DE-599)DOAJ38f38a8094cb4d4c9a95aad722767987 DE-627 ger DE-627 rakwb eng Shi Yong verfasserin aut An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The frequency response model of a microgrid system is an indispensable tool for designing secondary frequency controllers and analyzing system frequency stability. Owing to the complexity of a microgrid system structure, time‐varying operation state, and difficulty in obtaining the inverter parameters, it is difficult to model the microgrid system. To address the abovementioned problems, this study constructs the equivalent model of a microgrid system based on mechanism analysis and then identifies the equivalent model parameters using an online identification method based on a hybrid particle swarm optimization butterfly algorithm according to the sampled operation data to obtain the specific equivalent model of a microgrid for the controller. This modeling method has strong adaptability and can yield accurate identification modeling when the operational structure of the microgrid changes. butterfly algorithm microgrid online identification particle swarm optimization Technology T Science Q Cheng Yin verfasserin aut Su Jianhui verfasserin aut Mao Meiqin verfasserin aut In Energy Science & Engineering Wiley, 2014 11(2023), 5, Seite 1619-1629 (DE-627)750089202 (DE-600)2720339-6 20500505 nnns volume:11 year:2023 number:5 pages:1619-1629 https://doi.org/10.1002/ese3.1407 kostenfrei https://doaj.org/article/38f38a8094cb4d4c9a95aad722767987 kostenfrei https://doi.org/10.1002/ese3.1407 kostenfrei https://doaj.org/toc/2050-0505 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 5 1619-1629
allfieldsSound	10.1002/ese3.1407 doi (DE-627)DOAJ090454391 (DE-599)DOAJ38f38a8094cb4d4c9a95aad722767987 DE-627 ger DE-627 rakwb eng Shi Yong verfasserin aut An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The frequency response model of a microgrid system is an indispensable tool for designing secondary frequency controllers and analyzing system frequency stability. Owing to the complexity of a microgrid system structure, time‐varying operation state, and difficulty in obtaining the inverter parameters, it is difficult to model the microgrid system. To address the abovementioned problems, this study constructs the equivalent model of a microgrid system based on mechanism analysis and then identifies the equivalent model parameters using an online identification method based on a hybrid particle swarm optimization butterfly algorithm according to the sampled operation data to obtain the specific equivalent model of a microgrid for the controller. This modeling method has strong adaptability and can yield accurate identification modeling when the operational structure of the microgrid changes. butterfly algorithm microgrid online identification particle swarm optimization Technology T Science Q Cheng Yin verfasserin aut Su Jianhui verfasserin aut Mao Meiqin verfasserin aut In Energy Science & Engineering Wiley, 2014 11(2023), 5, Seite 1619-1629 (DE-627)750089202 (DE-600)2720339-6 20500505 nnns volume:11 year:2023 number:5 pages:1619-1629 https://doi.org/10.1002/ese3.1407 kostenfrei https://doaj.org/article/38f38a8094cb4d4c9a95aad722767987 kostenfrei https://doi.org/10.1002/ese3.1407 kostenfrei https://doaj.org/toc/2050-0505 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 5 1619-1629
language	English
source	In Energy Science & Engineering 11(2023), 5, Seite 1619-1629 volume:11 year:2023 number:5 pages:1619-1629
sourceStr	In Energy Science & Engineering 11(2023), 5, Seite 1619-1629 volume:11 year:2023 number:5 pages:1619-1629
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	butterfly algorithm microgrid online identification particle swarm optimization Technology T Science Q
isfreeaccess_bool	true
container_title	Energy Science & Engineering
authorswithroles_txt_mv	Shi Yong @@aut@@ Cheng Yin @@aut@@ Su Jianhui @@aut@@ Mao Meiqin @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	750089202
id	DOAJ090454391
language_de	englisch
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author	Shi Yong
spellingShingle	Shi Yong misc butterfly algorithm misc microgrid misc online identification misc particle swarm optimization misc Technology misc T misc Science misc Q An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm
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topic_title	An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm butterfly algorithm microgrid online identification particle swarm optimization
topic	misc butterfly algorithm misc microgrid misc online identification misc particle swarm optimization misc Technology misc T misc Science misc Q
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title	An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm
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title_full	An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm
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title_sort	online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm
title_auth	An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm
abstract	Abstract The frequency response model of a microgrid system is an indispensable tool for designing secondary frequency controllers and analyzing system frequency stability. Owing to the complexity of a microgrid system structure, time‐varying operation state, and difficulty in obtaining the inverter parameters, it is difficult to model the microgrid system. To address the abovementioned problems, this study constructs the equivalent model of a microgrid system based on mechanism analysis and then identifies the equivalent model parameters using an online identification method based on a hybrid particle swarm optimization butterfly algorithm according to the sampled operation data to obtain the specific equivalent model of a microgrid for the controller. This modeling method has strong adaptability and can yield accurate identification modeling when the operational structure of the microgrid changes.
abstractGer	Abstract The frequency response model of a microgrid system is an indispensable tool for designing secondary frequency controllers and analyzing system frequency stability. Owing to the complexity of a microgrid system structure, time‐varying operation state, and difficulty in obtaining the inverter parameters, it is difficult to model the microgrid system. To address the abovementioned problems, this study constructs the equivalent model of a microgrid system based on mechanism analysis and then identifies the equivalent model parameters using an online identification method based on a hybrid particle swarm optimization butterfly algorithm according to the sampled operation data to obtain the specific equivalent model of a microgrid for the controller. This modeling method has strong adaptability and can yield accurate identification modeling when the operational structure of the microgrid changes.
abstract_unstemmed	Abstract The frequency response model of a microgrid system is an indispensable tool for designing secondary frequency controllers and analyzing system frequency stability. Owing to the complexity of a microgrid system structure, time‐varying operation state, and difficulty in obtaining the inverter parameters, it is difficult to model the microgrid system. To address the abovementioned problems, this study constructs the equivalent model of a microgrid system based on mechanism analysis and then identifies the equivalent model parameters using an online identification method based on a hybrid particle swarm optimization butterfly algorithm according to the sampled operation data to obtain the specific equivalent model of a microgrid for the controller. This modeling method has strong adaptability and can yield accurate identification modeling when the operational structure of the microgrid changes.
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title_short	An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm
url	https://doi.org/10.1002/ese3.1407 https://doaj.org/article/38f38a8094cb4d4c9a95aad722767987 https://doaj.org/toc/2050-0505
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An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm

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