LQR-Based adaptive virtual inertia for grid integration of wind energy conversion system based on synchronverter model
This paper proposes adaptive virtual inertia for the synchronverter model implemented in a wind turbine generator system integrated into the grid through a back-to-back converter. A linear dynamic system is developed for the proposed adaptive virtual inertia, which employs the frequency deviation an...
- Autores:
-
González, Walter Gil
Montoya, Oscar Danilo
Escobar Mejía, Andrés
Hernández, Jesus C.
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/10369
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/10369
https://doi.org/10.3390/electronics10091022
- Palabra clave:
- Synchronverter
Virtual inertia
Frequency stability
Wind turbine generator system
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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LQR-Based adaptive virtual inertia for grid integration of wind energy conversion system based on synchronverter model |
| title |
LQR-Based adaptive virtual inertia for grid integration of wind energy conversion system based on synchronverter model |
| spellingShingle |
LQR-Based adaptive virtual inertia for grid integration of wind energy conversion system based on synchronverter model Synchronverter Virtual inertia Frequency stability Wind turbine generator system LEMB |
| title_short |
LQR-Based adaptive virtual inertia for grid integration of wind energy conversion system based on synchronverter model |
| title_full |
LQR-Based adaptive virtual inertia for grid integration of wind energy conversion system based on synchronverter model |
| title_fullStr |
LQR-Based adaptive virtual inertia for grid integration of wind energy conversion system based on synchronverter model |
| title_full_unstemmed |
LQR-Based adaptive virtual inertia for grid integration of wind energy conversion system based on synchronverter model |
| title_sort |
LQR-Based adaptive virtual inertia for grid integration of wind energy conversion system based on synchronverter model |
| dc.creator.fl_str_mv |
González, Walter Gil Montoya, Oscar Danilo Escobar Mejía, Andrés Hernández, Jesus C. |
| dc.contributor.author.none.fl_str_mv |
González, Walter Gil Montoya, Oscar Danilo Escobar Mejía, Andrés Hernández, Jesus C. |
| dc.subject.keywords.spa.fl_str_mv |
Synchronverter Virtual inertia Frequency stability Wind turbine generator system |
| topic |
Synchronverter Virtual inertia Frequency stability Wind turbine generator system LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
This paper proposes adaptive virtual inertia for the synchronverter model implemented in a wind turbine generator system integrated into the grid through a back-to-back converter. A linear dynamic system is developed for the proposed adaptive virtual inertia, which employs the frequency deviation and the rotor angle deviation of the synchronverter model as the state variables and the virtual inertia and frequency droop gain as the control variables. In addition, the proposed adaptive virtual inertia uses a linear quadratic regulator to ensure the optimal balance between fast frequency response and wind turbine generator system stress during disturbances. Hence, it minimizes frequency deviations with minimum effort. Several case simulations are proposed and carried out in MATLAB/Simulink software, and the results demonstrate the effectiveness and feasibility of the proposed adaptive virtual inertia synchronverter based on a linear quadratic regulator. The maximum and minimum frequency, the rate change of the frequency, and the integral of time-weighted absolute error are computed to quantify the performance of the proposed adaptive virtual inertia. These indexes are reduced by 46.61%, 52.67%, 79.41%, and 34.66%, in the worst case, when the proposed adaptive model is compared to the conventional synchronverter model. |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-09-28T14:27:44Z |
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2021-09-28T14:27:44Z |
| dc.date.issued.none.fl_str_mv |
2021-03-06 |
| dc.date.submitted.none.fl_str_mv |
2021-09-27 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/restrictedAccess |
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http://purl.org/coar/resource_type/c_6501 |
| dc.identifier.citation.spa.fl_str_mv |
Gil-González, W.; Montoya, O.D.; Escobar-Mejía, A.; Hernández, J.C. LQR-Based Adaptive Virtual Inertia for Grid Integration of Wind Energy Conversion System Based on Synchronverter Model. Electronics 2021, 10, 1022. https://doi.org/10.3390/electronics10091022 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/10369 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.3390/electronics10091022 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Gil-González, W.; Montoya, O.D.; Escobar-Mejía, A.; Hernández, J.C. LQR-Based Adaptive Virtual Inertia for Grid Integration of Wind Energy Conversion System Based on Synchronverter Model. Electronics 2021, 10, 1022. https://doi.org/10.3390/electronics10091022 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/10369 https://doi.org/10.3390/electronics10091022 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
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http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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16 páginas |
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application/pdf |
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Cartagena de Indias |
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Electronics 2021, 10, 1022 |
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Universidad Tecnológica de Bolívar |
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González, Walter Gil5380c267-9d69-4e5e-85cd-bb9dd81e0163Montoya, Oscar Danilo8a59ede1-6a4a-4d2e-abdc-d0afb14d4480Escobar Mejía, Andrés29523b05-add4-4221-96d8-373877dff51aHernández, Jesus C.0bddc46e-ce64-47d5-b654-2b2dfc3d87dc2021-09-28T14:27:44Z2021-09-28T14:27:44Z2021-03-062021-09-27Gil-González, W.; Montoya, O.D.; Escobar-Mejía, A.; Hernández, J.C. LQR-Based Adaptive Virtual Inertia for Grid Integration of Wind Energy Conversion System Based on Synchronverter Model. Electronics 2021, 10, 1022. https://doi.org/10.3390/electronics10091022https://hdl.handle.net/20.500.12585/10369https://doi.org/10.3390/electronics10091022Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis paper proposes adaptive virtual inertia for the synchronverter model implemented in a wind turbine generator system integrated into the grid through a back-to-back converter. A linear dynamic system is developed for the proposed adaptive virtual inertia, which employs the frequency deviation and the rotor angle deviation of the synchronverter model as the state variables and the virtual inertia and frequency droop gain as the control variables. In addition, the proposed adaptive virtual inertia uses a linear quadratic regulator to ensure the optimal balance between fast frequency response and wind turbine generator system stress during disturbances. Hence, it minimizes frequency deviations with minimum effort. Several case simulations are proposed and carried out in MATLAB/Simulink software, and the results demonstrate the effectiveness and feasibility of the proposed adaptive virtual inertia synchronverter based on a linear quadratic regulator. The maximum and minimum frequency, the rate change of the frequency, and the integral of time-weighted absolute error are computed to quantify the performance of the proposed adaptive virtual inertia. These indexes are reduced by 46.61%, 52.67%, 79.41%, and 34.66%, in the worst case, when the proposed adaptive model is compared to the conventional synchronverter model.16 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Electronics 2021, 10, 1022LQR-Based adaptive virtual inertia for grid integration of wind energy conversion system based on synchronverter modelinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1SynchronverterVirtual inertiaFrequency stabilityWind turbine generator systemLEMBCartagena de IndiasInvestigadoresBaran, J.; J ˛aderko, A. An MPPT Control of a PMSG-Based WECS with Disturbance Compensation and Wind Speed Estimation. Energies 2020, 13, 6344Yaramasu, V.; Wu, B.; Sen, P.C.; Kouro, S.; Narimani, M. High-power wind energy conversion systems: State-of-the-art and emerging technologies. Proc. IEEE 2015, 103, 740–788.Heier, S. Grid Integration of Wind Energy: Onshore and Offshore Conversion Systems; John Wiley & Sons: Hoboken, NJ, USA, 2014Marin-Hurtado, A.; Escobar-Mejía, A.; Gil-González, W. Adaptive Inertia for a Virtual Synchronous Machine Using an LQR Controller Applicable to a High-Voltage DC Terminal. In Proceedings of the 2020 IEEE ANDESCON, Quito, Ecuador, 13–16 October 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 1–6Kashem, S.B.A.; Chowdhury, M.E.; Ahmed, J.; Ashraf, A.; Shabrin, N. Wind Power Integration with Smart Grid and Storage System: Prospects and Limitations. Int. J. Adv. Comput. Sci. Appl. 2020, 11, 552–569.Amin, M.; Molinas, M. Self-synchronisation of wind farm in MMC-based HVDC system. In Proceedings of the 2016 IEEE Electrical Power and Energy Conference (EPEC), Ottawa, ON, Canada, 12–14 October 2016; IEEE: Piscataway, NJ, USA, 2016Do, T.D. Disturbance Observer-Based Fuzzy SMC of WECSs Without Wind Speed Measurement. IEEE Access 2017, 5, 147–155.Watil, A.; Magri, A.E.; Raihani, A.; Lajouad, R.; Giri, F. Multi-objective output feedback control strategy for a variable speed wind energy conversion system. Int. J. Electr. Power Energy Syst. 2020, 121, 106081Beltran, B.; Benbouzid, M.E.H.; Ahmed-Ali, T. Second-Order Sliding Mode Control of a Doubly Fed Induction Generator Driven Wind Turbine. IEEE Trans. Energy Convers. 2012, 27, 261–269.Majdoub, Y.; Abbou, A.; Akherraz, M. Variable speed control of DFIG-wind turbine with wind estimation. In Proceedings of the 2014 International Renewable and Sustainable Energy Conference (IRSEC), Ouarzazate, Morocco, 17–19 October 2014; IEEE: Piscataway, NJ, USA, 2014;Li, D.Y.; Cai, W.C.; Li, P.; Jia, Z.J.; Chen, H.J.; Song, Y.D. Neuroadaptive Variable Speed Control of Wind Turbine with Wind Speed Estimation. IEEE Trans. Ind. Electron. 2016, 63, 7754–7764.Song, D.; Yang, J.; Dong, M.; Joo, Y.H. Model predictive control with finite control set for variable-speed wind turbines. Energy 2017, 126, 564–572Boukhezzar, B.; Siguerdidjane, H. Nonlinear control with wind estimation of a DFIG variable speed wind turbine for power capture optimization. Energy Convers. Manag. 2009, 50, 885–892Boukhezzar, B.; Siguerdidjane, H. Comparison between linear and nonlinear control strategies for variable speed wind turbines. Control Eng. Pract. 2010, 18, 1357–1368Calabrese, D.; Tricarico, G.; Brescia, E.; Cascella, G.L.; Monopoli, V.G.; Cupertino, F. Variable Structure Control of a Small Ducted Wind Turbine in the Whole Wind Speed Range Using a Luenberger Observer. Energies 2020, 13, 4647Kim, Y.S.; Chung, I.Y.; Moon, S.I. Tuning of the PI Controller Parameters of a PMSG Wind Turbine to Improve Control Performance under Various Wind Speeds. Energies 2015, 8, 1406–1425Li, P.; Wang, J.; Xiong, L.; Wu, F. Nonlinear Controllers Based on Exact Feedback Linearization for Series-Compensated DFIG-Based Wind Parks to Mitigate Sub-Synchronous Control Interaction. Energies 2017, 10, 1182Kim, K.; Kim, H.G.; Song, Y.; Paek, I. Design and Simulation of an LQR-PI Control Algorithm for Medium Wind Turbine. Energies 2019, 12, 2248Yang, J.; Wu, J.; Dong, P.; Yang, J. Passivity-based control in wind turbine for maximal energy capture. In Proceedings of the 2004 IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies Proceedings, Hong Kong, China, 5–8 April 2004; IEEE: Piscataway, NJ, USA, 2004;Cisneros, R.; Gao, R.; Ortega, R.; Husain, I. A PI-passivity-based control of a wind energy conversion system enabled with a solid-state transformer. Int. J. Control 2020, 1–11Gao, R.; Husain, I.; Cisneros, R.; Ortega, R. Passivity-based and standard PI controls application to wind energy conversion system. In Proceedings of the 2016 IEEE Energy Conversion Congress and Exposition (ECCE), Milwaukee, WI, USA, 18–22 September 2016; IEEE: Piscataway, NJ, USA, 2016;Mancilla-David, F.; Ortega, R. Adaptive passivity-based control for maximum power extraction of stand-alone windmill systems. Control Eng. Pract. 2012, 20, 173–181Azeem, B.; Rehman, F.; Mehmood, C.; Ali, S.; Khan, B.; Saeed, S. Exact Feedback Linearization (EFL) and De-Couple Control of Doubly Fed Induction Generator Based Wind Turbine. In Proceedings of the 2016 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 19–21 December 2016; IEEE: Piscataway, NJ, USA, 2016Jose, J.T.; Chattopadhyay, A.B. Mathematical Formulation of Feedback Linearizing Control of Doubly Fed Induction Generator Including Magnetic Saturation Effects. Math. Probl. Eng. 2020, 2020, 1–10Suul, J.A.; D’Arco, S.; Rodriguez, P.; Molinas, M. Extended stability range of weak grids with Voltage Source Converters through impedance-conditioned grid synchronization. In Proceedings of the 11th IET International Conference on AC and DC Power Transmission, Birmingham, UK, 10–12 February 2015Amin, M.; Rygg, A.; Molinas, M. Self-synchronization of wind farm in an MMC-based HVDC system: A stability investigation. IEEE Trans. Energy Convers. 2017, 32, 458–470.Zhong, Q.C.; Weiss, G. Synchronverters: Inverters That Mimic Synchronous Generators. IEEE Trans. Ind. Electron. 2011, 58, 1259–1267Zhong, Q.C.; Ma, Z.; Ming, W.L.; Konstantopoulos, G.C. Grid-friendly wind power systems based on the synchronverter technology. Energy Convers. Manag. 2015, 89, 719–726.Yap, K.Y.; Sarimuthu, C.R.; Lim, J.M.Y. Grid Integration of Solar Photovoltaic System Using Machine Learning-Based Virtual Inertia Synthetization in Synchronverter. IEEE Access 2020, 8, 49961–49976Aouini, R.; Marinescu, B.; Kilani, K.B.; Elleuch, M. Synchronverter-based emulation and control of HVDC transmission. IEEE Trans. Power Syst. 2015, 31, 278–286Zhong, Q.C.; Nguyen, P.L.; Ma, Z.; Sheng, W. Self-synchronized synchronverters: Inverters without a dedicated synchronization unit. IEEE Trans. Power Electron. 2013, 29, 617–630Wang, C.; Meng, J.; Wang, Y.; Wang, H. Adaptive virtual inertia control for DC microgrid with variable droop coefficient. In Proceedings of the 2017 20th International Conference on Electrical Machines and Systems (ICEMS), Sydney, NSW, Australia, 11–14 August 2017; IEEE: Piscataway, NJ, USA, 2017Blau, M.; Weiss, G. Synchronverters used for damping inter-area oscillations in two-area power systems. Renew. Energy Power Qual. J. 2018, 45–50Gil-González, W.; Garcés, A.; Fosso, O.B. Passivity-based control for small hydro-power generation with PMSG and VSC. IEEE Access 2020, 8, 153001–153010Anderson, B.D.; Moore, J.B. Optimal Control: Linear Quadratic Methods; Courier Corporation: North Chelmsford, MA, USA, 2007http://purl.org/coar/resource_type/c_2df8fbb1ORIGINAL[Art. 20] LQR-Based Adaptive Virtual Inertia_Oscar Danilo Montoya.pdf[Art. 20] LQR-Based Adaptive Virtual Inertia_Oscar Danilo Montoya.pdfapplication/pdf1344560https://repositorio.utb.edu.co/bitstream/20.500.12585/10369/1/%5bArt.%2020%5d%20LQR-Based%20Adaptive%20Virtual%20Inertia_Oscar%20Danilo%20Montoya.pdfcfcaf2f2d26a8e39aab8aac67d00cbdaMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/10369/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/10369/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXT[Art. 20] LQR-Based Adaptive Virtual Inertia_Oscar Danilo Montoya.pdf.txt[Art. 20] LQR-Based Adaptive Virtual Inertia_Oscar Danilo Montoya.pdf.txtExtracted 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