Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter

This paper proposes a disturbance observer-based robust model predictive control (MPC) for a voltage sensorless grid-connected inverter with an inductive-capacitive-inductive (LCL) filter. A full-state estimator and a grid voltage observer are designed to reduce the number of sensors. A lumped distu...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Nguyen Ngoc Nam [verfasserIn] Ngoc-Duc Nguyen [verfasserIn] Changwoo Yoon [verfasserIn] Young Il Lee [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Sensorless model predictive control (MPC) disturbance observer three-phase inverters inductive-capacitive-inductive (LCL)

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 9(2021), Seite 109793-109805
Übergeordnetes Werk:	volume:9 ; year:2021 ; pages:109793-109805

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2021.3102628

Katalog-ID:	DOAJ071341587

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ071341587
003	DE-627
005	20230309101256.0
007	cr uuu---uuuuu
008	230228s2021 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1109/ACCESS.2021.3102628 \|2 doi
035			\|a (DE-627)DOAJ071341587
035			\|a (DE-599)DOAJce53847ad3af43b3bebbcee6bc4315ec
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TK1-9971
100	0		\|a Nguyen Ngoc Nam \|e verfasserin \|4 aut
245	1	0	\|a Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter
264		1	\|c 2021
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a This paper proposes a disturbance observer-based robust model predictive control (MPC) for a voltage sensorless grid-connected inverter with an inductive-capacitive-inductive (LCL) filter. A full-state estimator and a grid voltage observer are designed to reduce the number of sensors. A lumped disturbance observer, considering the parameter mismatch along with the grid impedance variation, is also designed to eliminate the steady-state error. A cost function, which consists of the error state and control input, is employed in the MPC design. Based on the Lyapunov stability, the full-state observer, voltage estimation, lumped disturbance observer, and the robust controller gains are obtained by solving an optimization problem based on linear matrix inequality (LMI). A frequency response analysis of the entire system is conducted to verify the reference tracking and disturbance rejection outcomes. As a result, the state and grid voltage observer outcomes converge to the actual values as rapidly as possible. The effectiveness of the proposed control method is demonstrated in comparison with the proportional-integral (PI) approach and with a controller recently proposed in the literature. Simulation and experimental results are presented to verify the effectiveness of the proposed method under LCL parameter uncertainties and grid impedance variations.
650		4	\|a Sensorless model predictive control (MPC)
650		4	\|a disturbance observer
650		4	\|a three-phase inverters
650		4	\|a inductive-capacitive-inductive (LCL)
653		0	\|a Electrical engineering. Electronics. Nuclear engineering
700	0		\|a Ngoc-Duc Nguyen \|e verfasserin \|4 aut
700	0		\|a Changwoo Yoon \|e verfasserin \|4 aut
700	0		\|a Young Il Lee \|e verfasserin \|4 aut
773	0	8	\|i In \|t IEEE Access \|d IEEE, 2014 \|g 9(2021), Seite 109793-109805 \|w (DE-627)728440385 \|w (DE-600)2687964-5 \|x 21693536 \|7 nnns
773	1	8	\|g volume:9 \|g year:2021 \|g pages:109793-109805
856	4	0	\|u https://doi.org/10.1109/ACCESS.2021.3102628 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/ce53847ad3af43b3bebbcee6bc4315ec \|z kostenfrei
856	4	0	\|u https://ieeexplore.ieee.org/document/9507463/ \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2169-3536 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
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_39
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_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 9 \|j 2021 \|h 109793-109805

Indexfelder

author_variant	n n n nnn n d n ndn c y cy y i l yil
matchkey_str	article:21693536:2021----::itracosrebsdoutoepeitvcnrloaotgsnolsgicn
hierarchy_sort_str	2021
callnumber-subject-code	TK
publishDate	2021
allfields	10.1109/ACCESS.2021.3102628 doi (DE-627)DOAJ071341587 (DE-599)DOAJce53847ad3af43b3bebbcee6bc4315ec DE-627 ger DE-627 rakwb eng TK1-9971 Nguyen Ngoc Nam verfasserin aut Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a disturbance observer-based robust model predictive control (MPC) for a voltage sensorless grid-connected inverter with an inductive-capacitive-inductive (LCL) filter. A full-state estimator and a grid voltage observer are designed to reduce the number of sensors. A lumped disturbance observer, considering the parameter mismatch along with the grid impedance variation, is also designed to eliminate the steady-state error. A cost function, which consists of the error state and control input, is employed in the MPC design. Based on the Lyapunov stability, the full-state observer, voltage estimation, lumped disturbance observer, and the robust controller gains are obtained by solving an optimization problem based on linear matrix inequality (LMI). A frequency response analysis of the entire system is conducted to verify the reference tracking and disturbance rejection outcomes. As a result, the state and grid voltage observer outcomes converge to the actual values as rapidly as possible. The effectiveness of the proposed control method is demonstrated in comparison with the proportional-integral (PI) approach and with a controller recently proposed in the literature. Simulation and experimental results are presented to verify the effectiveness of the proposed method under LCL parameter uncertainties and grid impedance variations. Sensorless model predictive control (MPC) disturbance observer three-phase inverters inductive-capacitive-inductive (LCL) Electrical engineering. Electronics. Nuclear engineering Ngoc-Duc Nguyen verfasserin aut Changwoo Yoon verfasserin aut Young Il Lee verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 109793-109805 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:109793-109805 https://doi.org/10.1109/ACCESS.2021.3102628 kostenfrei https://doaj.org/article/ce53847ad3af43b3bebbcee6bc4315ec kostenfrei https://ieeexplore.ieee.org/document/9507463/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 109793-109805
spelling	10.1109/ACCESS.2021.3102628 doi (DE-627)DOAJ071341587 (DE-599)DOAJce53847ad3af43b3bebbcee6bc4315ec DE-627 ger DE-627 rakwb eng TK1-9971 Nguyen Ngoc Nam verfasserin aut Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a disturbance observer-based robust model predictive control (MPC) for a voltage sensorless grid-connected inverter with an inductive-capacitive-inductive (LCL) filter. A full-state estimator and a grid voltage observer are designed to reduce the number of sensors. A lumped disturbance observer, considering the parameter mismatch along with the grid impedance variation, is also designed to eliminate the steady-state error. A cost function, which consists of the error state and control input, is employed in the MPC design. Based on the Lyapunov stability, the full-state observer, voltage estimation, lumped disturbance observer, and the robust controller gains are obtained by solving an optimization problem based on linear matrix inequality (LMI). A frequency response analysis of the entire system is conducted to verify the reference tracking and disturbance rejection outcomes. As a result, the state and grid voltage observer outcomes converge to the actual values as rapidly as possible. The effectiveness of the proposed control method is demonstrated in comparison with the proportional-integral (PI) approach and with a controller recently proposed in the literature. Simulation and experimental results are presented to verify the effectiveness of the proposed method under LCL parameter uncertainties and grid impedance variations. Sensorless model predictive control (MPC) disturbance observer three-phase inverters inductive-capacitive-inductive (LCL) Electrical engineering. Electronics. Nuclear engineering Ngoc-Duc Nguyen verfasserin aut Changwoo Yoon verfasserin aut Young Il Lee verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 109793-109805 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:109793-109805 https://doi.org/10.1109/ACCESS.2021.3102628 kostenfrei https://doaj.org/article/ce53847ad3af43b3bebbcee6bc4315ec kostenfrei https://ieeexplore.ieee.org/document/9507463/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 109793-109805
allfields_unstemmed	10.1109/ACCESS.2021.3102628 doi (DE-627)DOAJ071341587 (DE-599)DOAJce53847ad3af43b3bebbcee6bc4315ec DE-627 ger DE-627 rakwb eng TK1-9971 Nguyen Ngoc Nam verfasserin aut Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a disturbance observer-based robust model predictive control (MPC) for a voltage sensorless grid-connected inverter with an inductive-capacitive-inductive (LCL) filter. A full-state estimator and a grid voltage observer are designed to reduce the number of sensors. A lumped disturbance observer, considering the parameter mismatch along with the grid impedance variation, is also designed to eliminate the steady-state error. A cost function, which consists of the error state and control input, is employed in the MPC design. Based on the Lyapunov stability, the full-state observer, voltage estimation, lumped disturbance observer, and the robust controller gains are obtained by solving an optimization problem based on linear matrix inequality (LMI). A frequency response analysis of the entire system is conducted to verify the reference tracking and disturbance rejection outcomes. As a result, the state and grid voltage observer outcomes converge to the actual values as rapidly as possible. The effectiveness of the proposed control method is demonstrated in comparison with the proportional-integral (PI) approach and with a controller recently proposed in the literature. Simulation and experimental results are presented to verify the effectiveness of the proposed method under LCL parameter uncertainties and grid impedance variations. Sensorless model predictive control (MPC) disturbance observer three-phase inverters inductive-capacitive-inductive (LCL) Electrical engineering. Electronics. Nuclear engineering Ngoc-Duc Nguyen verfasserin aut Changwoo Yoon verfasserin aut Young Il Lee verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 109793-109805 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:109793-109805 https://doi.org/10.1109/ACCESS.2021.3102628 kostenfrei https://doaj.org/article/ce53847ad3af43b3bebbcee6bc4315ec kostenfrei https://ieeexplore.ieee.org/document/9507463/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 109793-109805
allfieldsGer	10.1109/ACCESS.2021.3102628 doi (DE-627)DOAJ071341587 (DE-599)DOAJce53847ad3af43b3bebbcee6bc4315ec DE-627 ger DE-627 rakwb eng TK1-9971 Nguyen Ngoc Nam verfasserin aut Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a disturbance observer-based robust model predictive control (MPC) for a voltage sensorless grid-connected inverter with an inductive-capacitive-inductive (LCL) filter. A full-state estimator and a grid voltage observer are designed to reduce the number of sensors. A lumped disturbance observer, considering the parameter mismatch along with the grid impedance variation, is also designed to eliminate the steady-state error. A cost function, which consists of the error state and control input, is employed in the MPC design. Based on the Lyapunov stability, the full-state observer, voltage estimation, lumped disturbance observer, and the robust controller gains are obtained by solving an optimization problem based on linear matrix inequality (LMI). A frequency response analysis of the entire system is conducted to verify the reference tracking and disturbance rejection outcomes. As a result, the state and grid voltage observer outcomes converge to the actual values as rapidly as possible. The effectiveness of the proposed control method is demonstrated in comparison with the proportional-integral (PI) approach and with a controller recently proposed in the literature. Simulation and experimental results are presented to verify the effectiveness of the proposed method under LCL parameter uncertainties and grid impedance variations. Sensorless model predictive control (MPC) disturbance observer three-phase inverters inductive-capacitive-inductive (LCL) Electrical engineering. Electronics. Nuclear engineering Ngoc-Duc Nguyen verfasserin aut Changwoo Yoon verfasserin aut Young Il Lee verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 109793-109805 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:109793-109805 https://doi.org/10.1109/ACCESS.2021.3102628 kostenfrei https://doaj.org/article/ce53847ad3af43b3bebbcee6bc4315ec kostenfrei https://ieeexplore.ieee.org/document/9507463/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 109793-109805
allfieldsSound	10.1109/ACCESS.2021.3102628 doi (DE-627)DOAJ071341587 (DE-599)DOAJce53847ad3af43b3bebbcee6bc4315ec DE-627 ger DE-627 rakwb eng TK1-9971 Nguyen Ngoc Nam verfasserin aut Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a disturbance observer-based robust model predictive control (MPC) for a voltage sensorless grid-connected inverter with an inductive-capacitive-inductive (LCL) filter. A full-state estimator and a grid voltage observer are designed to reduce the number of sensors. A lumped disturbance observer, considering the parameter mismatch along with the grid impedance variation, is also designed to eliminate the steady-state error. A cost function, which consists of the error state and control input, is employed in the MPC design. Based on the Lyapunov stability, the full-state observer, voltage estimation, lumped disturbance observer, and the robust controller gains are obtained by solving an optimization problem based on linear matrix inequality (LMI). A frequency response analysis of the entire system is conducted to verify the reference tracking and disturbance rejection outcomes. As a result, the state and grid voltage observer outcomes converge to the actual values as rapidly as possible. The effectiveness of the proposed control method is demonstrated in comparison with the proportional-integral (PI) approach and with a controller recently proposed in the literature. Simulation and experimental results are presented to verify the effectiveness of the proposed method under LCL parameter uncertainties and grid impedance variations. Sensorless model predictive control (MPC) disturbance observer three-phase inverters inductive-capacitive-inductive (LCL) Electrical engineering. Electronics. Nuclear engineering Ngoc-Duc Nguyen verfasserin aut Changwoo Yoon verfasserin aut Young Il Lee verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 109793-109805 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:109793-109805 https://doi.org/10.1109/ACCESS.2021.3102628 kostenfrei https://doaj.org/article/ce53847ad3af43b3bebbcee6bc4315ec kostenfrei https://ieeexplore.ieee.org/document/9507463/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 109793-109805
language	English
source	In IEEE Access 9(2021), Seite 109793-109805 volume:9 year:2021 pages:109793-109805
sourceStr	In IEEE Access 9(2021), Seite 109793-109805 volume:9 year:2021 pages:109793-109805
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Sensorless model predictive control (MPC) disturbance observer three-phase inverters inductive-capacitive-inductive (LCL) Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	IEEE Access
authorswithroles_txt_mv	Nguyen Ngoc Nam @@aut@@ Ngoc-Duc Nguyen @@aut@@ Changwoo Yoon @@aut@@ Young Il Lee @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	728440385
id	DOAJ071341587
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">DOAJ071341587</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309101256.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2021.3102628</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ071341587</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJce53847ad3af43b3bebbcee6bc4315ec</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Nguyen Ngoc Nam</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</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">This paper proposes a disturbance observer-based robust model predictive control (MPC) for a voltage sensorless grid-connected inverter with an inductive-capacitive-inductive (LCL) filter. A full-state estimator and a grid voltage observer are designed to reduce the number of sensors. A lumped disturbance observer, considering the parameter mismatch along with the grid impedance variation, is also designed to eliminate the steady-state error. A cost function, which consists of the error state and control input, is employed in the MPC design. Based on the Lyapunov stability, the full-state observer, voltage estimation, lumped disturbance observer, and the robust controller gains are obtained by solving an optimization problem based on linear matrix inequality (LMI). A frequency response analysis of the entire system is conducted to verify the reference tracking and disturbance rejection outcomes. As a result, the state and grid voltage observer outcomes converge to the actual values as rapidly as possible. The effectiveness of the proposed control method is demonstrated in comparison with the proportional-integral (PI) approach and with a controller recently proposed in the literature. Simulation and experimental results are presented to verify the effectiveness of the proposed method under LCL parameter uncertainties and grid impedance variations.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensorless model predictive control (MPC)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">disturbance observer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">three-phase inverters</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">inductive-capacitive-inductive (LCL)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ngoc-Duc Nguyen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Changwoo Yoon</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Young Il Lee</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">9(2021), Seite 109793-109805</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:109793-109805</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2021.3102628</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/ce53847ad3af43b3bebbcee6bc4315ec</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9507463/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</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_39</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_95</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_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</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_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</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_4249</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_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</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_4325</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_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">9</subfield><subfield code="j">2021</subfield><subfield code="h">109793-109805</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Nguyen Ngoc Nam
spellingShingle	Nguyen Ngoc Nam misc TK1-9971 misc Sensorless model predictive control (MPC) misc disturbance observer misc three-phase inverters misc inductive-capacitive-inductive (LCL) misc Electrical engineering. Electronics. Nuclear engineering Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter
authorStr	Nguyen Ngoc Nam
ppnlink_with_tag_str_mv	@@773@@(DE-627)728440385
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	TK1-9971
illustrated	Not Illustrated
issn	21693536
topic_title	TK1-9971 Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter Sensorless model predictive control (MPC) disturbance observer three-phase inverters inductive-capacitive-inductive (LCL)
topic	misc TK1-9971 misc Sensorless model predictive control (MPC) misc disturbance observer misc three-phase inverters misc inductive-capacitive-inductive (LCL) misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Sensorless model predictive control (MPC) misc disturbance observer misc three-phase inverters misc inductive-capacitive-inductive (LCL) misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Sensorless model predictive control (MPC) misc disturbance observer misc three-phase inverters misc inductive-capacitive-inductive (LCL) misc Electrical engineering. Electronics. Nuclear engineering
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	IEEE Access
hierarchy_parent_id	728440385
hierarchy_top_title	IEEE Access
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)728440385 (DE-600)2687964-5
title	Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter
ctrlnum	(DE-627)DOAJ071341587 (DE-599)DOAJce53847ad3af43b3bebbcee6bc4315ec
title_full	Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter
author_sort	Nguyen Ngoc Nam
journal	IEEE Access
journalStr	IEEE Access
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2021
contenttype_str_mv	txt
container_start_page	109793
author_browse	Nguyen Ngoc Nam Ngoc-Duc Nguyen Changwoo Yoon Young Il Lee
container_volume	9
class	TK1-9971
format_se	Elektronische Aufsätze
author-letter	Nguyen Ngoc Nam
doi_str_mv	10.1109/ACCESS.2021.3102628
author2-role	verfasserin
title_sort	disturbance observer-based robust model predictive control for a voltage sensorless grid-connected inverter with an lcl filter
callnumber	TK1-9971
title_auth	Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter
abstract	This paper proposes a disturbance observer-based robust model predictive control (MPC) for a voltage sensorless grid-connected inverter with an inductive-capacitive-inductive (LCL) filter. A full-state estimator and a grid voltage observer are designed to reduce the number of sensors. A lumped disturbance observer, considering the parameter mismatch along with the grid impedance variation, is also designed to eliminate the steady-state error. A cost function, which consists of the error state and control input, is employed in the MPC design. Based on the Lyapunov stability, the full-state observer, voltage estimation, lumped disturbance observer, and the robust controller gains are obtained by solving an optimization problem based on linear matrix inequality (LMI). A frequency response analysis of the entire system is conducted to verify the reference tracking and disturbance rejection outcomes. As a result, the state and grid voltage observer outcomes converge to the actual values as rapidly as possible. The effectiveness of the proposed control method is demonstrated in comparison with the proportional-integral (PI) approach and with a controller recently proposed in the literature. Simulation and experimental results are presented to verify the effectiveness of the proposed method under LCL parameter uncertainties and grid impedance variations.
abstractGer	This paper proposes a disturbance observer-based robust model predictive control (MPC) for a voltage sensorless grid-connected inverter with an inductive-capacitive-inductive (LCL) filter. A full-state estimator and a grid voltage observer are designed to reduce the number of sensors. A lumped disturbance observer, considering the parameter mismatch along with the grid impedance variation, is also designed to eliminate the steady-state error. A cost function, which consists of the error state and control input, is employed in the MPC design. Based on the Lyapunov stability, the full-state observer, voltage estimation, lumped disturbance observer, and the robust controller gains are obtained by solving an optimization problem based on linear matrix inequality (LMI). A frequency response analysis of the entire system is conducted to verify the reference tracking and disturbance rejection outcomes. As a result, the state and grid voltage observer outcomes converge to the actual values as rapidly as possible. The effectiveness of the proposed control method is demonstrated in comparison with the proportional-integral (PI) approach and with a controller recently proposed in the literature. Simulation and experimental results are presented to verify the effectiveness of the proposed method under LCL parameter uncertainties and grid impedance variations.
abstract_unstemmed	This paper proposes a disturbance observer-based robust model predictive control (MPC) for a voltage sensorless grid-connected inverter with an inductive-capacitive-inductive (LCL) filter. A full-state estimator and a grid voltage observer are designed to reduce the number of sensors. A lumped disturbance observer, considering the parameter mismatch along with the grid impedance variation, is also designed to eliminate the steady-state error. A cost function, which consists of the error state and control input, is employed in the MPC design. Based on the Lyapunov stability, the full-state observer, voltage estimation, lumped disturbance observer, and the robust controller gains are obtained by solving an optimization problem based on linear matrix inequality (LMI). A frequency response analysis of the entire system is conducted to verify the reference tracking and disturbance rejection outcomes. As a result, the state and grid voltage observer outcomes converge to the actual values as rapidly as possible. The effectiveness of the proposed control method is demonstrated in comparison with the proportional-integral (PI) approach and with a controller recently proposed in the literature. Simulation and experimental results are presented to verify the effectiveness of the proposed method under LCL parameter uncertainties and grid impedance variations.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter
url	https://doi.org/10.1109/ACCESS.2021.3102628 https://doaj.org/article/ce53847ad3af43b3bebbcee6bc4315ec https://ieeexplore.ieee.org/document/9507463/ https://doaj.org/toc/2169-3536
remote_bool	true
author2	Ngoc-Duc Nguyen Changwoo Yoon Young Il Lee
author2Str	Ngoc-Duc Nguyen Changwoo Yoon Young Il Lee
ppnlink	728440385
callnumber-subject	TK - Electrical and Nuclear Engineering
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1109/ACCESS.2021.3102628
callnumber-a	TK1-9971
up_date	2024-07-03T19:44:26.661Z
_version_	1803588328922021888
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">DOAJ071341587</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309101256.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2021.3102628</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ071341587</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJce53847ad3af43b3bebbcee6bc4315ec</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Nguyen Ngoc Nam</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</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">This paper proposes a disturbance observer-based robust model predictive control (MPC) for a voltage sensorless grid-connected inverter with an inductive-capacitive-inductive (LCL) filter. A full-state estimator and a grid voltage observer are designed to reduce the number of sensors. A lumped disturbance observer, considering the parameter mismatch along with the grid impedance variation, is also designed to eliminate the steady-state error. A cost function, which consists of the error state and control input, is employed in the MPC design. Based on the Lyapunov stability, the full-state observer, voltage estimation, lumped disturbance observer, and the robust controller gains are obtained by solving an optimization problem based on linear matrix inequality (LMI). A frequency response analysis of the entire system is conducted to verify the reference tracking and disturbance rejection outcomes. As a result, the state and grid voltage observer outcomes converge to the actual values as rapidly as possible. The effectiveness of the proposed control method is demonstrated in comparison with the proportional-integral (PI) approach and with a controller recently proposed in the literature. Simulation and experimental results are presented to verify the effectiveness of the proposed method under LCL parameter uncertainties and grid impedance variations.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensorless model predictive control (MPC)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">disturbance observer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">three-phase inverters</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">inductive-capacitive-inductive (LCL)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ngoc-Duc Nguyen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Changwoo Yoon</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Young Il Lee</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">9(2021), Seite 109793-109805</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:109793-109805</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2021.3102628</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/ce53847ad3af43b3bebbcee6bc4315ec</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9507463/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</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_39</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_95</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_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</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_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</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_4249</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_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</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_4325</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_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">9</subfield><subfield code="j">2021</subfield><subfield code="h">109793-109805</subfield></datafield></record></collection>
score	7.3987885

Nicht das Richtige dabei?

Schreiben Sie uns!

Disturbance Observer-Based Robust Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With an LCL Filter

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?