Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems

In this work, the performance of a fractional-order integral sliding mode controller (FOISMC) has been assessed for frequency regulation of interconnected power systems considering inherent nonlinearities, such as generation rate constraint and governor dead-band. The performance of the proposed con...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Patel, Vivek [verfasserIn] Guha, Dipayan [verfasserIn] Purwar, Shubhi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Frequency regulation Chebvshev neural network Wind turbine generator Fractional-order integral sliding mode controller Estimation

Übergeordnetes Werk:	Enthalten in: Computers & electrical engineering - Amsterdam [u.a.] : Elsevier Science, 1973, 96
Übergeordnetes Werk:	volume:96

DOI / URN:	10.1016/j.compeleceng.2021.107534

Katalog-ID:	ELV006895573

Internformat


LEADER	01000caa a22002652 4500
001	ELV006895573
003	DE-627
005	20230524165057.0
007	cr uuu---uuuuu
008	230506s2021 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.compeleceng.2021.107534 \|2 doi
035			\|a (DE-627)ELV006895573
035			\|a (ELSEVIER)S0045-7906(21)00479-1
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 620 \|q DE-600
084			\|a 53.00 \|2 bkl
084			\|a 35.06 \|2 bkl
084			\|a 54.00 \|2 bkl
100	1		\|a Patel, Vivek \|e verfasserin \|4 aut
245	1	0	\|a Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems
264		1	\|c 2021
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a In this work, the performance of a fractional-order integral sliding mode controller (FOISMC) has been assessed for frequency regulation of interconnected power systems considering inherent nonlinearities, such as generation rate constraint and governor dead-band. The performance of the proposed controller has been measured on two- and three-area interconnected power systems integrated with a wind turbine generator. A Chebyshev neural network (NN)-based estimator is designed to estimate the lumped system uncertainties, including nonlinearities, unknown external disturbances, and parameter uncertainty. Afterwards, an improved FOISMC is developed, augmenting the estimated output of the NN-based estimator to cope with system disturbances effectively. To corroborate the potential benefits, the results obtained with NN-aided FOISMC (NN-FOISMC) are compared with the outputs of FOISMC and results reported in the literature. The simulation study confirms the superiority of NN-FOISMC over its other counterparts in terms of damping of power-frequency oscillations, weaker chattering, and a high degree of robustness.
650		4	\|a Frequency regulation
650		4	\|a Chebvshev neural network
650		4	\|a Wind turbine generator
650		4	\|a Fractional-order integral sliding mode controller
650		4	\|a Estimation
700	1		\|a Guha, Dipayan \|e verfasserin \|4 aut
700	1		\|a Purwar, Shubhi \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Computers & electrical engineering \|d Amsterdam [u.a.] : Elsevier Science, 1973 \|g 96 \|h Online-Ressource \|w (DE-627)306715872 \|w (DE-600)1501325-X \|w (DE-576)094531293 \|x 1879-0755 \|7 nnns
773	1	8	\|g volume:96
912			\|a GBV_USEFLAG_U
912			\|a SYSFLAG_U
912			\|a GBV_ELV
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 53.00 \|j Elektrotechnik: Allgemeines
936	b	k	\|a 35.06 \|j Computeranwendungen \|x Chemie
936	b	k	\|a 54.00 \|j Informatik: Allgemeines
951			\|a AR
952			\|d 96

Indexfelder

author_variant	v p vp d g dg s p sp
matchkey_str	article:18790755:2021----::erlewraddrcinlresiigoeotolrofeunyeua
hierarchy_sort_str	2021
bklnumber	53.00 35.06 54.00
publishDate	2021
allfields	10.1016/j.compeleceng.2021.107534 doi (DE-627)ELV006895573 (ELSEVIER)S0045-7906(21)00479-1 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Patel, Vivek verfasserin aut Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the performance of a fractional-order integral sliding mode controller (FOISMC) has been assessed for frequency regulation of interconnected power systems considering inherent nonlinearities, such as generation rate constraint and governor dead-band. The performance of the proposed controller has been measured on two- and three-area interconnected power systems integrated with a wind turbine generator. A Chebyshev neural network (NN)-based estimator is designed to estimate the lumped system uncertainties, including nonlinearities, unknown external disturbances, and parameter uncertainty. Afterwards, an improved FOISMC is developed, augmenting the estimated output of the NN-based estimator to cope with system disturbances effectively. To corroborate the potential benefits, the results obtained with NN-aided FOISMC (NN-FOISMC) are compared with the outputs of FOISMC and results reported in the literature. The simulation study confirms the superiority of NN-FOISMC over its other counterparts in terms of damping of power-frequency oscillations, weaker chattering, and a high degree of robustness. Frequency regulation Chebvshev neural network Wind turbine generator Fractional-order integral sliding mode controller Estimation Guha, Dipayan verfasserin aut Purwar, Shubhi verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 96 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:96 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 96
spelling	10.1016/j.compeleceng.2021.107534 doi (DE-627)ELV006895573 (ELSEVIER)S0045-7906(21)00479-1 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Patel, Vivek verfasserin aut Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the performance of a fractional-order integral sliding mode controller (FOISMC) has been assessed for frequency regulation of interconnected power systems considering inherent nonlinearities, such as generation rate constraint and governor dead-band. The performance of the proposed controller has been measured on two- and three-area interconnected power systems integrated with a wind turbine generator. A Chebyshev neural network (NN)-based estimator is designed to estimate the lumped system uncertainties, including nonlinearities, unknown external disturbances, and parameter uncertainty. Afterwards, an improved FOISMC is developed, augmenting the estimated output of the NN-based estimator to cope with system disturbances effectively. To corroborate the potential benefits, the results obtained with NN-aided FOISMC (NN-FOISMC) are compared with the outputs of FOISMC and results reported in the literature. The simulation study confirms the superiority of NN-FOISMC over its other counterparts in terms of damping of power-frequency oscillations, weaker chattering, and a high degree of robustness. Frequency regulation Chebvshev neural network Wind turbine generator Fractional-order integral sliding mode controller Estimation Guha, Dipayan verfasserin aut Purwar, Shubhi verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 96 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:96 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 96
allfields_unstemmed	10.1016/j.compeleceng.2021.107534 doi (DE-627)ELV006895573 (ELSEVIER)S0045-7906(21)00479-1 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Patel, Vivek verfasserin aut Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the performance of a fractional-order integral sliding mode controller (FOISMC) has been assessed for frequency regulation of interconnected power systems considering inherent nonlinearities, such as generation rate constraint and governor dead-band. The performance of the proposed controller has been measured on two- and three-area interconnected power systems integrated with a wind turbine generator. A Chebyshev neural network (NN)-based estimator is designed to estimate the lumped system uncertainties, including nonlinearities, unknown external disturbances, and parameter uncertainty. Afterwards, an improved FOISMC is developed, augmenting the estimated output of the NN-based estimator to cope with system disturbances effectively. To corroborate the potential benefits, the results obtained with NN-aided FOISMC (NN-FOISMC) are compared with the outputs of FOISMC and results reported in the literature. The simulation study confirms the superiority of NN-FOISMC over its other counterparts in terms of damping of power-frequency oscillations, weaker chattering, and a high degree of robustness. Frequency regulation Chebvshev neural network Wind turbine generator Fractional-order integral sliding mode controller Estimation Guha, Dipayan verfasserin aut Purwar, Shubhi verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 96 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:96 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 96
allfieldsGer	10.1016/j.compeleceng.2021.107534 doi (DE-627)ELV006895573 (ELSEVIER)S0045-7906(21)00479-1 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Patel, Vivek verfasserin aut Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the performance of a fractional-order integral sliding mode controller (FOISMC) has been assessed for frequency regulation of interconnected power systems considering inherent nonlinearities, such as generation rate constraint and governor dead-band. The performance of the proposed controller has been measured on two- and three-area interconnected power systems integrated with a wind turbine generator. A Chebyshev neural network (NN)-based estimator is designed to estimate the lumped system uncertainties, including nonlinearities, unknown external disturbances, and parameter uncertainty. Afterwards, an improved FOISMC is developed, augmenting the estimated output of the NN-based estimator to cope with system disturbances effectively. To corroborate the potential benefits, the results obtained with NN-aided FOISMC (NN-FOISMC) are compared with the outputs of FOISMC and results reported in the literature. The simulation study confirms the superiority of NN-FOISMC over its other counterparts in terms of damping of power-frequency oscillations, weaker chattering, and a high degree of robustness. Frequency regulation Chebvshev neural network Wind turbine generator Fractional-order integral sliding mode controller Estimation Guha, Dipayan verfasserin aut Purwar, Shubhi verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 96 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:96 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 96
allfieldsSound	10.1016/j.compeleceng.2021.107534 doi (DE-627)ELV006895573 (ELSEVIER)S0045-7906(21)00479-1 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Patel, Vivek verfasserin aut Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the performance of a fractional-order integral sliding mode controller (FOISMC) has been assessed for frequency regulation of interconnected power systems considering inherent nonlinearities, such as generation rate constraint and governor dead-band. The performance of the proposed controller has been measured on two- and three-area interconnected power systems integrated with a wind turbine generator. A Chebyshev neural network (NN)-based estimator is designed to estimate the lumped system uncertainties, including nonlinearities, unknown external disturbances, and parameter uncertainty. Afterwards, an improved FOISMC is developed, augmenting the estimated output of the NN-based estimator to cope with system disturbances effectively. To corroborate the potential benefits, the results obtained with NN-aided FOISMC (NN-FOISMC) are compared with the outputs of FOISMC and results reported in the literature. The simulation study confirms the superiority of NN-FOISMC over its other counterparts in terms of damping of power-frequency oscillations, weaker chattering, and a high degree of robustness. Frequency regulation Chebvshev neural network Wind turbine generator Fractional-order integral sliding mode controller Estimation Guha, Dipayan verfasserin aut Purwar, Shubhi verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 96 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:96 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 96
language	English
source	Enthalten in Computers & electrical engineering 96 volume:96
sourceStr	Enthalten in Computers & electrical engineering 96 volume:96
format_phy_str_mv	Article
bklname	Elektrotechnik: Allgemeines Computeranwendungen Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	Frequency regulation Chebvshev neural network Wind turbine generator Fractional-order integral sliding mode controller Estimation
dewey-raw	620
isfreeaccess_bool	false
container_title	Computers & electrical engineering
authorswithroles_txt_mv	Patel, Vivek @@aut@@ Guha, Dipayan @@aut@@ Purwar, Shubhi @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	306715872
dewey-sort	3620
id	ELV006895573
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV006895573</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524165057.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230506s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.compeleceng.2021.107534</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV006895573</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0045-7906(21)00479-1</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.06</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Patel, Vivek</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this work, the performance of a fractional-order integral sliding mode controller (FOISMC) has been assessed for frequency regulation of interconnected power systems considering inherent nonlinearities, such as generation rate constraint and governor dead-band. The performance of the proposed controller has been measured on two- and three-area interconnected power systems integrated with a wind turbine generator. A Chebyshev neural network (NN)-based estimator is designed to estimate the lumped system uncertainties, including nonlinearities, unknown external disturbances, and parameter uncertainty. Afterwards, an improved FOISMC is developed, augmenting the estimated output of the NN-based estimator to cope with system disturbances effectively. To corroborate the potential benefits, the results obtained with NN-aided FOISMC (NN-FOISMC) are compared with the outputs of FOISMC and results reported in the literature. The simulation study confirms the superiority of NN-FOISMC over its other counterparts in terms of damping of power-frequency oscillations, weaker chattering, and a high degree of robustness.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Frequency regulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Chebvshev neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wind turbine generator</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fractional-order integral sliding mode controller</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Estimation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guha, Dipayan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Purwar, Shubhi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computers & electrical engineering</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1973</subfield><subfield code="g">96</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)306715872</subfield><subfield code="w">(DE-600)1501325-X</subfield><subfield code="w">(DE-576)094531293</subfield><subfield code="x">1879-0755</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:96</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">53.00</subfield><subfield code="j">Elektrotechnik: Allgemeines</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.06</subfield><subfield code="j">Computeranwendungen</subfield><subfield code="x">Chemie</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="j">Informatik: Allgemeines</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">96</subfield></datafield></record></collection>
author	Patel, Vivek
spellingShingle	Patel, Vivek ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc Frequency regulation misc Chebvshev neural network misc Wind turbine generator misc Fractional-order integral sliding mode controller misc Estimation Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems
authorStr	Patel, Vivek
ppnlink_with_tag_str_mv	@@773@@(DE-627)306715872
format	electronic Article
dewey-ones	620 - Engineering & allied operations
delete_txt_mv	keep
author_role	aut aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
issn	1879-0755
topic_title	620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems Frequency regulation Chebvshev neural network Wind turbine generator Fractional-order integral sliding mode controller Estimation
topic	ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc Frequency regulation misc Chebvshev neural network misc Wind turbine generator misc Fractional-order integral sliding mode controller misc Estimation
topic_unstemmed	ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc Frequency regulation misc Chebvshev neural network misc Wind turbine generator misc Fractional-order integral sliding mode controller misc Estimation
topic_browse	ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc Frequency regulation misc Chebvshev neural network misc Wind turbine generator misc Fractional-order integral sliding mode controller misc Estimation
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Computers & electrical engineering
hierarchy_parent_id	306715872
dewey-tens	620 - Engineering
hierarchy_top_title	Computers & electrical engineering
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)306715872 (DE-600)1501325-X (DE-576)094531293
title	Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems
ctrlnum	(DE-627)ELV006895573 (ELSEVIER)S0045-7906(21)00479-1
title_full	Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems
author_sort	Patel, Vivek
journal	Computers & electrical engineering
journalStr	Computers & electrical engineering
lang_code	eng
isOA_bool	false
dewey-hundreds	600 - Technology
recordtype	marc
publishDateSort	2021
contenttype_str_mv	zzz
author_browse	Patel, Vivek Guha, Dipayan Purwar, Shubhi
container_volume	96
class	620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl
format_se	Elektronische Aufsätze
author-letter	Patel, Vivek
doi_str_mv	10.1016/j.compeleceng.2021.107534
dewey-full	620
author2-role	verfasserin
title_sort	neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems
title_auth	Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems
abstract	In this work, the performance of a fractional-order integral sliding mode controller (FOISMC) has been assessed for frequency regulation of interconnected power systems considering inherent nonlinearities, such as generation rate constraint and governor dead-band. The performance of the proposed controller has been measured on two- and three-area interconnected power systems integrated with a wind turbine generator. A Chebyshev neural network (NN)-based estimator is designed to estimate the lumped system uncertainties, including nonlinearities, unknown external disturbances, and parameter uncertainty. Afterwards, an improved FOISMC is developed, augmenting the estimated output of the NN-based estimator to cope with system disturbances effectively. To corroborate the potential benefits, the results obtained with NN-aided FOISMC (NN-FOISMC) are compared with the outputs of FOISMC and results reported in the literature. The simulation study confirms the superiority of NN-FOISMC over its other counterparts in terms of damping of power-frequency oscillations, weaker chattering, and a high degree of robustness.
abstractGer	In this work, the performance of a fractional-order integral sliding mode controller (FOISMC) has been assessed for frequency regulation of interconnected power systems considering inherent nonlinearities, such as generation rate constraint and governor dead-band. The performance of the proposed controller has been measured on two- and three-area interconnected power systems integrated with a wind turbine generator. A Chebyshev neural network (NN)-based estimator is designed to estimate the lumped system uncertainties, including nonlinearities, unknown external disturbances, and parameter uncertainty. Afterwards, an improved FOISMC is developed, augmenting the estimated output of the NN-based estimator to cope with system disturbances effectively. To corroborate the potential benefits, the results obtained with NN-aided FOISMC (NN-FOISMC) are compared with the outputs of FOISMC and results reported in the literature. The simulation study confirms the superiority of NN-FOISMC over its other counterparts in terms of damping of power-frequency oscillations, weaker chattering, and a high degree of robustness.
abstract_unstemmed	In this work, the performance of a fractional-order integral sliding mode controller (FOISMC) has been assessed for frequency regulation of interconnected power systems considering inherent nonlinearities, such as generation rate constraint and governor dead-band. The performance of the proposed controller has been measured on two- and three-area interconnected power systems integrated with a wind turbine generator. A Chebyshev neural network (NN)-based estimator is designed to estimate the lumped system uncertainties, including nonlinearities, unknown external disturbances, and parameter uncertainty. Afterwards, an improved FOISMC is developed, augmenting the estimated output of the NN-based estimator to cope with system disturbances effectively. To corroborate the potential benefits, the results obtained with NN-aided FOISMC (NN-FOISMC) are compared with the outputs of FOISMC and results reported in the literature. The simulation study confirms the superiority of NN-FOISMC over its other counterparts in terms of damping of power-frequency oscillations, weaker chattering, and a high degree of robustness.
collection_details	GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393
title_short	Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems
remote_bool	true
author2	Guha, Dipayan Purwar, Shubhi
author2Str	Guha, Dipayan Purwar, Shubhi
ppnlink	306715872
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.compeleceng.2021.107534
up_date	2024-07-06T22:55:11.234Z
_version_	1803872120324751360
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV006895573</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524165057.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230506s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.compeleceng.2021.107534</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV006895573</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0045-7906(21)00479-1</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.06</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Patel, Vivek</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this work, the performance of a fractional-order integral sliding mode controller (FOISMC) has been assessed for frequency regulation of interconnected power systems considering inherent nonlinearities, such as generation rate constraint and governor dead-band. The performance of the proposed controller has been measured on two- and three-area interconnected power systems integrated with a wind turbine generator. A Chebyshev neural network (NN)-based estimator is designed to estimate the lumped system uncertainties, including nonlinearities, unknown external disturbances, and parameter uncertainty. Afterwards, an improved FOISMC is developed, augmenting the estimated output of the NN-based estimator to cope with system disturbances effectively. To corroborate the potential benefits, the results obtained with NN-aided FOISMC (NN-FOISMC) are compared with the outputs of FOISMC and results reported in the literature. The simulation study confirms the superiority of NN-FOISMC over its other counterparts in terms of damping of power-frequency oscillations, weaker chattering, and a high degree of robustness.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Frequency regulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Chebvshev neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wind turbine generator</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fractional-order integral sliding mode controller</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Estimation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guha, Dipayan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Purwar, Shubhi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computers & electrical engineering</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1973</subfield><subfield code="g">96</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)306715872</subfield><subfield code="w">(DE-600)1501325-X</subfield><subfield code="w">(DE-576)094531293</subfield><subfield code="x">1879-0755</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:96</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">53.00</subfield><subfield code="j">Elektrotechnik: Allgemeines</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.06</subfield><subfield code="j">Computeranwendungen</subfield><subfield code="x">Chemie</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="j">Informatik: Allgemeines</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">96</subfield></datafield></record></collection>
score	7.4002676

Nicht das Richtige dabei?

Schreiben Sie uns!

Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?