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
Autor*in: |
Patel, Vivek [verfasserIn] Guha, Dipayan [verfasserIn] Purwar, Shubhi [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Computers & electrical engineering - Amsterdam [u.a.] : Elsevier Science, 1973, 96 |
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Übergeordnetes Werk: |
volume:96 |
DOI / URN: |
10.1016/j.compeleceng.2021.107534 |
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Katalog-ID: |
ELV006895573 |
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245 | 1 | 0 | |a Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems |
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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 | |
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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 |
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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 |
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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 |
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Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems |
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Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems |
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Patel, Vivek |
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Patel, Vivek Guha, Dipayan Purwar, Shubhi |
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Elektronische Aufsätze |
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Patel, Vivek |
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10.1016/j.compeleceng.2021.107534 |
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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. |
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title_short |
Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems |
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up_date |
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