A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Model Parameter Mismatch in Three-Phase APF
Model predictive control (MPC) methods are widely used in the power electronic control field, including finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). The degree of parameter uncertainty influence on the two methods is the key to...
Ausführliche Beschreibung
Autor*in: |
Jianfeng Yang [verfasserIn] Yang Liu [verfasserIn] Rui Yan [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Schlagwörter: |
finite control set- model predictive control |
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Übergeordnetes Werk: |
In: Frontiers in Energy Research - Frontiers Media S.A., 2014, 9(2021) |
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Übergeordnetes Werk: |
volume:9 ; year:2021 |
Links: |
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DOI / URN: |
10.3389/fenrg.2021.727364 |
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Katalog-ID: |
DOAJ057504601 |
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10.3389/fenrg.2021.727364 doi (DE-627)DOAJ057504601 (DE-599)DOAJ218a673978bf4c19bbb3c7ae291ed90a DE-627 ger DE-627 rakwb eng Jianfeng Yang verfasserin aut A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Model Parameter Mismatch in Three-Phase APF 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Model predictive control (MPC) methods are widely used in the power electronic control field, including finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). The degree of parameter uncertainty influence on the two methods is the key to evaluate the feasibility of the two methods in power electronic application. This paper proposes a research method to analyze FCS-MPC and CCS-MPC’s influence on the current prediction error of three-phase active power filter (APF) under parameter uncertainty. It compares the performance of the two model predictive control methods under parameters uncertainty. In each sampling period of the prediction algorithm, different prediction error conditions will be produced when FCS-MPC cycles the candidate vectors. Different pulse width modulation (PWM) results will be produced when CCS-MPC solves the quadratic programming (QP) problem. This paper presents the simulation results and discusses the influence of inaccurate modeling of load resistance and inductance parameters on the control performance of the two MPC algorithms, the influence of reference value and state value on prediction error is also compared. The prediction error caused by resistance mismatch is lower than that caused by inductance mismatch, more errors are caused by underestimating inductance values than by overestimating inductance values. The CCS-MPC has a better control effect and dynamic performance in parameter mismatch, and the influence of parameter mismatch is relatively tiny. active power filter model predictive control power quality finite control set- model predictive control continuous control set model predictive control error analyses General Works A Yang Liu verfasserin aut Rui Yan verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2021) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2021 https://doi.org/10.3389/fenrg.2021.727364 kostenfrei https://doaj.org/article/218a673978bf4c19bbb3c7ae291ed90a kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.727364/full kostenfrei https://doaj.org/toc/2296-598X 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_2003 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 |
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10.3389/fenrg.2021.727364 doi (DE-627)DOAJ057504601 (DE-599)DOAJ218a673978bf4c19bbb3c7ae291ed90a DE-627 ger DE-627 rakwb eng Jianfeng Yang verfasserin aut A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Model Parameter Mismatch in Three-Phase APF 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Model predictive control (MPC) methods are widely used in the power electronic control field, including finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). The degree of parameter uncertainty influence on the two methods is the key to evaluate the feasibility of the two methods in power electronic application. This paper proposes a research method to analyze FCS-MPC and CCS-MPC’s influence on the current prediction error of three-phase active power filter (APF) under parameter uncertainty. It compares the performance of the two model predictive control methods under parameters uncertainty. In each sampling period of the prediction algorithm, different prediction error conditions will be produced when FCS-MPC cycles the candidate vectors. Different pulse width modulation (PWM) results will be produced when CCS-MPC solves the quadratic programming (QP) problem. This paper presents the simulation results and discusses the influence of inaccurate modeling of load resistance and inductance parameters on the control performance of the two MPC algorithms, the influence of reference value and state value on prediction error is also compared. The prediction error caused by resistance mismatch is lower than that caused by inductance mismatch, more errors are caused by underestimating inductance values than by overestimating inductance values. The CCS-MPC has a better control effect and dynamic performance in parameter mismatch, and the influence of parameter mismatch is relatively tiny. active power filter model predictive control power quality finite control set- model predictive control continuous control set model predictive control error analyses General Works A Yang Liu verfasserin aut Rui Yan verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2021) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2021 https://doi.org/10.3389/fenrg.2021.727364 kostenfrei https://doaj.org/article/218a673978bf4c19bbb3c7ae291ed90a kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.727364/full kostenfrei https://doaj.org/toc/2296-598X 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_2003 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 |
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10.3389/fenrg.2021.727364 doi (DE-627)DOAJ057504601 (DE-599)DOAJ218a673978bf4c19bbb3c7ae291ed90a DE-627 ger DE-627 rakwb eng Jianfeng Yang verfasserin aut A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Model Parameter Mismatch in Three-Phase APF 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Model predictive control (MPC) methods are widely used in the power electronic control field, including finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). The degree of parameter uncertainty influence on the two methods is the key to evaluate the feasibility of the two methods in power electronic application. This paper proposes a research method to analyze FCS-MPC and CCS-MPC’s influence on the current prediction error of three-phase active power filter (APF) under parameter uncertainty. It compares the performance of the two model predictive control methods under parameters uncertainty. In each sampling period of the prediction algorithm, different prediction error conditions will be produced when FCS-MPC cycles the candidate vectors. Different pulse width modulation (PWM) results will be produced when CCS-MPC solves the quadratic programming (QP) problem. This paper presents the simulation results and discusses the influence of inaccurate modeling of load resistance and inductance parameters on the control performance of the two MPC algorithms, the influence of reference value and state value on prediction error is also compared. The prediction error caused by resistance mismatch is lower than that caused by inductance mismatch, more errors are caused by underestimating inductance values than by overestimating inductance values. The CCS-MPC has a better control effect and dynamic performance in parameter mismatch, and the influence of parameter mismatch is relatively tiny. active power filter model predictive control power quality finite control set- model predictive control continuous control set model predictive control error analyses General Works A Yang Liu verfasserin aut Rui Yan verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2021) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2021 https://doi.org/10.3389/fenrg.2021.727364 kostenfrei https://doaj.org/article/218a673978bf4c19bbb3c7ae291ed90a kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.727364/full kostenfrei https://doaj.org/toc/2296-598X 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_2003 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 |
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10.3389/fenrg.2021.727364 doi (DE-627)DOAJ057504601 (DE-599)DOAJ218a673978bf4c19bbb3c7ae291ed90a DE-627 ger DE-627 rakwb eng Jianfeng Yang verfasserin aut A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Model Parameter Mismatch in Three-Phase APF 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Model predictive control (MPC) methods are widely used in the power electronic control field, including finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). The degree of parameter uncertainty influence on the two methods is the key to evaluate the feasibility of the two methods in power electronic application. This paper proposes a research method to analyze FCS-MPC and CCS-MPC’s influence on the current prediction error of three-phase active power filter (APF) under parameter uncertainty. It compares the performance of the two model predictive control methods under parameters uncertainty. In each sampling period of the prediction algorithm, different prediction error conditions will be produced when FCS-MPC cycles the candidate vectors. Different pulse width modulation (PWM) results will be produced when CCS-MPC solves the quadratic programming (QP) problem. This paper presents the simulation results and discusses the influence of inaccurate modeling of load resistance and inductance parameters on the control performance of the two MPC algorithms, the influence of reference value and state value on prediction error is also compared. The prediction error caused by resistance mismatch is lower than that caused by inductance mismatch, more errors are caused by underestimating inductance values than by overestimating inductance values. The CCS-MPC has a better control effect and dynamic performance in parameter mismatch, and the influence of parameter mismatch is relatively tiny. active power filter model predictive control power quality finite control set- model predictive control continuous control set model predictive control error analyses General Works A Yang Liu verfasserin aut Rui Yan verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2021) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2021 https://doi.org/10.3389/fenrg.2021.727364 kostenfrei https://doaj.org/article/218a673978bf4c19bbb3c7ae291ed90a kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.727364/full kostenfrei https://doaj.org/toc/2296-598X 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_2003 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 |
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10.3389/fenrg.2021.727364 doi (DE-627)DOAJ057504601 (DE-599)DOAJ218a673978bf4c19bbb3c7ae291ed90a DE-627 ger DE-627 rakwb eng Jianfeng Yang verfasserin aut A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Model Parameter Mismatch in Three-Phase APF 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Model predictive control (MPC) methods are widely used in the power electronic control field, including finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). The degree of parameter uncertainty influence on the two methods is the key to evaluate the feasibility of the two methods in power electronic application. This paper proposes a research method to analyze FCS-MPC and CCS-MPC’s influence on the current prediction error of three-phase active power filter (APF) under parameter uncertainty. It compares the performance of the two model predictive control methods under parameters uncertainty. In each sampling period of the prediction algorithm, different prediction error conditions will be produced when FCS-MPC cycles the candidate vectors. Different pulse width modulation (PWM) results will be produced when CCS-MPC solves the quadratic programming (QP) problem. This paper presents the simulation results and discusses the influence of inaccurate modeling of load resistance and inductance parameters on the control performance of the two MPC algorithms, the influence of reference value and state value on prediction error is also compared. The prediction error caused by resistance mismatch is lower than that caused by inductance mismatch, more errors are caused by underestimating inductance values than by overestimating inductance values. The CCS-MPC has a better control effect and dynamic performance in parameter mismatch, and the influence of parameter mismatch is relatively tiny. active power filter model predictive control power quality finite control set- model predictive control continuous control set model predictive control error analyses General Works A Yang Liu verfasserin aut Rui Yan verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2021) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2021 https://doi.org/10.3389/fenrg.2021.727364 kostenfrei https://doaj.org/article/218a673978bf4c19bbb3c7ae291ed90a kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.727364/full kostenfrei https://doaj.org/toc/2296-598X 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_2003 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 |
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Jianfeng Yang |
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Jianfeng Yang misc active power filter misc model predictive control misc power quality misc finite control set- model predictive control misc continuous control set model predictive control misc error analyses misc General Works misc A A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Model Parameter Mismatch in Three-Phase APF |
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A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Model Parameter Mismatch in Three-Phase APF active power filter model predictive control power quality finite control set- model predictive control continuous control set model predictive control error analyses |
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comparison of finite control set and continuous control set model predictive control schemes for model parameter mismatch in three-phase apf |
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A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Model Parameter Mismatch in Three-Phase APF |
abstract |
Model predictive control (MPC) methods are widely used in the power electronic control field, including finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). The degree of parameter uncertainty influence on the two methods is the key to evaluate the feasibility of the two methods in power electronic application. This paper proposes a research method to analyze FCS-MPC and CCS-MPC’s influence on the current prediction error of three-phase active power filter (APF) under parameter uncertainty. It compares the performance of the two model predictive control methods under parameters uncertainty. In each sampling period of the prediction algorithm, different prediction error conditions will be produced when FCS-MPC cycles the candidate vectors. Different pulse width modulation (PWM) results will be produced when CCS-MPC solves the quadratic programming (QP) problem. This paper presents the simulation results and discusses the influence of inaccurate modeling of load resistance and inductance parameters on the control performance of the two MPC algorithms, the influence of reference value and state value on prediction error is also compared. The prediction error caused by resistance mismatch is lower than that caused by inductance mismatch, more errors are caused by underestimating inductance values than by overestimating inductance values. The CCS-MPC has a better control effect and dynamic performance in parameter mismatch, and the influence of parameter mismatch is relatively tiny. |
abstractGer |
Model predictive control (MPC) methods are widely used in the power electronic control field, including finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). The degree of parameter uncertainty influence on the two methods is the key to evaluate the feasibility of the two methods in power electronic application. This paper proposes a research method to analyze FCS-MPC and CCS-MPC’s influence on the current prediction error of three-phase active power filter (APF) under parameter uncertainty. It compares the performance of the two model predictive control methods under parameters uncertainty. In each sampling period of the prediction algorithm, different prediction error conditions will be produced when FCS-MPC cycles the candidate vectors. Different pulse width modulation (PWM) results will be produced when CCS-MPC solves the quadratic programming (QP) problem. This paper presents the simulation results and discusses the influence of inaccurate modeling of load resistance and inductance parameters on the control performance of the two MPC algorithms, the influence of reference value and state value on prediction error is also compared. The prediction error caused by resistance mismatch is lower than that caused by inductance mismatch, more errors are caused by underestimating inductance values than by overestimating inductance values. The CCS-MPC has a better control effect and dynamic performance in parameter mismatch, and the influence of parameter mismatch is relatively tiny. |
abstract_unstemmed |
Model predictive control (MPC) methods are widely used in the power electronic control field, including finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). The degree of parameter uncertainty influence on the two methods is the key to evaluate the feasibility of the two methods in power electronic application. This paper proposes a research method to analyze FCS-MPC and CCS-MPC’s influence on the current prediction error of three-phase active power filter (APF) under parameter uncertainty. It compares the performance of the two model predictive control methods under parameters uncertainty. In each sampling period of the prediction algorithm, different prediction error conditions will be produced when FCS-MPC cycles the candidate vectors. Different pulse width modulation (PWM) results will be produced when CCS-MPC solves the quadratic programming (QP) problem. This paper presents the simulation results and discusses the influence of inaccurate modeling of load resistance and inductance parameters on the control performance of the two MPC algorithms, the influence of reference value and state value on prediction error is also compared. The prediction error caused by resistance mismatch is lower than that caused by inductance mismatch, more errors are caused by underestimating inductance values than by overestimating inductance values. The CCS-MPC has a better control effect and dynamic performance in parameter mismatch, and the influence of parameter mismatch is relatively tiny. |
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title_short |
A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Model Parameter Mismatch in Three-Phase APF |
url |
https://doi.org/10.3389/fenrg.2021.727364 https://doaj.org/article/218a673978bf4c19bbb3c7ae291ed90a https://www.frontiersin.org/articles/10.3389/fenrg.2021.727364/full https://doaj.org/toc/2296-598X |
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