Applications of Digital Twins in Power Systems: A Perspective

Data science-based digital twin models of renewable energy system technologies developed in a real-time data-rich environment help develop better decisions and predictions than those in the present environment. Based on this real-time analysis of countrywide data, digital twin contributes to effecti...

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Autores:: Kamyabi, Leila
Lie, Tek Tjing
Madanian, Samaneh

Tipo de recurso:: Article of journal

Fecha de publicación:: 2022

Institución:: Universidad Tecnológica de Bolívar

Repositorio:: Repositorio Institucional UTB

Idioma:: eng

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network_acronym_str	UTB2
network_name_str	Repositorio Institucional UTB
repository_id_str
dc.title.spa.fl_str_mv	Applications of Digital Twins in Power Systems: A Perspective
dc.title.translated.spa.fl_str_mv	Applications of Digital Twins in Power Systems: A Perspective
title	Applications of Digital Twins in Power Systems: A Perspective
spellingShingle	Applications of Digital Twins in Power Systems: A Perspective Data Sciences Digital Twin Power Systems solar PV and Wind Turbine Generation
title_short	Applications of Digital Twins in Power Systems: A Perspective
title_full	Applications of Digital Twins in Power Systems: A Perspective
title_fullStr	Applications of Digital Twins in Power Systems: A Perspective
title_full_unstemmed	Applications of Digital Twins in Power Systems: A Perspective
title_sort	Applications of Digital Twins in Power Systems: A Perspective
dc.creator.fl_str_mv	Kamyabi, Leila Lie, Tek Tjing Madanian, Samaneh
dc.contributor.author.eng.fl_str_mv	Kamyabi, Leila Lie, Tek Tjing Madanian, Samaneh
dc.subject.eng.fl_str_mv	Data Sciences Digital Twin Power Systems solar PV and Wind Turbine Generation
topic	Data Sciences Digital Twin Power Systems solar PV and Wind Turbine Generation
description	Data science-based digital twin models of renewable energy system technologies developed in a real-time data-rich environment help develop better decisions and predictions than those in the present environment. Based on this real-time analysis of countrywide data, digital twin contributes to effective and reduced cost-based power system control at the localised level. Developing digital twin models from the collection of relevant data is an innovative technology. The challenge is how to leverage all the operational data and analyse the use of data from across transmission and distribution networks to help achieve the objectives. This paper presents an overview of the existing applications of digital twins in power systems.
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-07-28 16:45:26 2025-05-21T19:15:45Z
dc.date.available.none.fl_str_mv	2022-07-28 16:45:26
dc.date.issued.none.fl_str_mv	2022-07-28
dc.type.spa.fl_str_mv	Artículo de revista
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dc.type.local.eng.fl_str_mv	Journal article
dc.type.content.eng.fl_str_mv	Text
dc.type.version.eng.fl_str_mv	info:eu-repo/semantics/publishedVersion
dc.type.coarversion.eng.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
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dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12585/13503
dc.identifier.url.none.fl_str_mv	https://doi.org/10.32397/tesea.vol3.n2.484
dc.identifier.doi.none.fl_str_mv	10.32397/tesea.vol3.n2.484
dc.identifier.eissn.none.fl_str_mv	2745-0120
url	https://hdl.handle.net/20.500.12585/13503 https://doi.org/10.32397/tesea.vol3.n2.484
identifier_str_mv	10.32397/tesea.vol3.n2.484 2745-0120
dc.language.iso.eng.fl_str_mv	eng
language	eng
dc.relation.references.eng.fl_str_mv	Panayiotis Moutis and Omid Alizadeh-Mousavi. Digital twin of distribution power transformer for real-time monitoring of medium voltage from low voltage measurements. IEEE Transactions on Power Delivery, 36(4):1952-1963, 2021. [2] Independent Group of Scientists appointed by the Secretary-General. The Future is Now: Science for Achieving Sustainable Development (GSDR 2019). Technical report, United Nations, 2019. [3] Carlos Henrique dos Santos, José Arnaldo Barra Montevechi, José Antônio de Queiroz, Rafael de Carvalho Miranda, and Fabiano Leal. Decision support in productive processes through des and abs in the digital twin era: a systematic literature review. International Journal of Production Research, 60(8):2662-2681, 2022. [4] Anton Rassõlkin, Tamas Orosz, Galina Lvovna Demidova, Vladimir Kuts, Viktor Rjabtšikov, Toomas Vaimann, and Ants Kallaste. Implementation of digital twins for electrical energy conversion systems in selected case studies. Proceedings of the Estonian Academy of Sciences, 70(1):19 - 39, 2021. [5] Barbara Rita Barricelli, Elena Casiraghi, and Daniela Fogli. A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE Access, 7:167653-167671, 2019. [6] Aidan Fuller, Zhong Fan, Charles Day, and Chris Barlow. Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8:108952-108971, 2020. [7] Vivi Qiuchen Lu, Ajith Kumar Parlikad, Philip Woodall, Gishan Don Ranasinghe, and James Heaton. Developing a dynamic digital twin at a building level: Using cambridge campus as case study. In International conference on smart infrastructure and construction 2019 (ICSIC) driving data-informed decision-making, pages 67-75. ICE Publishing, 2019. [8] Greyce N. Schroeder, Charles Steinmetz, Carlos E. Pereira, and Danubia B. Espindola. Digital twin data modeling with automationml and a communication methodology for data exchange. IFAC-PapersOnLine, 49(30):12-17, 2016. 4th IFAC Symposium on Telematics Applications TA 2016. [9] Huaming Pan, Zhenlan Dou, Yanxing Cai, Wenzhu Li, Xing Lei, and Dong Han. Digital twin and its application in power system. In 2020 5th International Conference on Power and Renewable Energy (ICPRE), pages 21-26, 2020. [10] Ali Aghazadeh Ardebili, Antonella Longo, and Antonio Ficarella. Digital twin (dt) in smart energy systems-systematic literature review of dt as a growing solution for energy internet of the things (eiot). E3S Web of Conferences, 312:09002, 2021. [11] Wei Zhou. Research on wireless sensor network access control and load balancing in the industrial digital twin scenario. Journal of Sensors, 2022:3929958, 2022. [12] Xueyong Tang, Yi Ding, Jinyong Lei, He Yang, and Yankan Song. Dynamic load balancing method based on optimal complete matching of weighted bipartite graph for simulation tasks in multi-energy system digital twin applications. Energy Reports, 8:1423-1431, 2022. [13] Yasmin Fathy, Mona Jaber, and Zunaira Nadeem. Digital twin-driven decision making and planning for energy consumption. Journal of Sensor and Actuator Networks, 10(2), 2021. [14] Hyang-A Park, Gilsung Byeon, Wanbin Son, Hyung-Chul Jo, Jongyul Kim, and Sungshin Kim. Digital twin for operation of microgrid: Optimal scheduling in virtual space of digital twin. Energies, 13(20):5504, 2020. [15] A A Smirnov, V S Lunenko, and I A Boldyrev. Application of digital twins of equipment for managing complex renewable energy. IOP Conference Series: Materials Science and Engineering, 1035(1):012023, 2021. [16] Saran Ganesh, Arcadio Perilla, Jose Rueda Torres, Peter Palensky, and Mart van der Meijden. Validation of emt digital twin models for dynamic voltage performance assessment of 66 kv offshore transmission network. Applied Sciences, 11(1), 2021. [17] Palak Jain, Jason Poon, Jai Prakash Singh, Costas Spanos, Seth R. Sanders, and Sanjib Kumar Panda. A digital twin approach for fault diagnosis in distributed photovoltaic systems. IEEE Transactions on Power Electronics, 35(1):940-956, 2020. [18] Kamel Arafet and Rafael Berlanga. Digital twins in solar farms: An approach through time series and deep learning. Algorithms, 14(5), 2021. [19] Yuan He, Junchen Guo, and Xiaolong Zheng. From surveillance to digital twin: Challenges and recent advances of signal processing for industrial internet of things. IEEE Signal Processing Magazine, 35(5):120-129, 2018. [20] Xiang Xie, Ajith Kumar Parlikad, and Ramprakash Srinivasan Puri. A neural ordinary differential equations based approach for demand forecasting within power grid digital twins. In 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pages 1-6, 2019. [21] Edward O'Dwyer, Indranil Pan, Richard Charlesworth, Sarah Butler, and Nilay Shah. Integration of an energy management tool and digital twin for coordination and control of multi-vector smart energy systems. Sustainable Cities and Society, 62:102412, 2020. [22] Minglei You, Qian Wang, Hongjian Sun, Iván Castro, and Jing Jiang. Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties. Applied Energy, 305:117899, 2022. [23] Yan Xu. A review of cyber security risks of power systems: from static to dynamic false data attacks. Protection and Control of Modern Power Systems, 5(1):1-12, 2020. [24] Ahmed Saad, Samy Faddel, Tarek Youssef, and Osama A. Mohammed. On the implementation of iot-based digital twin for networked microgrids resiliency against cyber attacks. IEEE Transactions on Smart Grid, 11(6):5138-5150, 2020. [25] Fei Tao, Meng Zhang, Yushan Liu, and A.Y.C. Nee. Digital twin driven prognostics and health management for complex equipment. CIRP Annals, 67(1):169-172, 2018. [26] Krishnamoorthi Sivalingam, Marco Sepulveda, Mark Spring, and Peter Davies. A review and methodology development for remaining useful life prediction of offshore fixed and floating wind turbine power converter with digital twin technology perspective. In 2018 2nd International Conference on Green Energy and Applications (ICGEA), pages 197-204, 2018. [27] Hergen Pargmann, Dörthe Euhausen, and Robin Faber. Intelligent big data processing for wind farm monitoring and analysis based on cloud-technologies and digital twins: A quantitative approach. In 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), pages 233-237, 2018. [28] Yong Yang, Zhu Chen, Jing Yan, Zhi Xiong, Jun Zhang, Hongxia Yuan, Yali Tu, and Tianyun Zhang. State evaluation of power transformer based on digital twin. In 2019 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pages 230-235. IEEE, 2019. [29] Farid K. Moghadam and Amir R. Nejad. Online condition monitoring of floating wind turbines drivetrain by means of digital twin. Mechanical Systems and Signal Processing, 162:108087, 2022. [30] Amir Ebrahimi. Challenges of developing a digital twin model of renewable energy generators. In 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), pages 1059-1066, 2019. [31] ORACLE. Digital twins for iot applications a comprehensive approach to implementing iot digital twins. Technical report, ORACLE White Paper, 2017. [32] W. Yang. 18 - condition monitoring of offshore wind turbines. In Chong Ng and Li Ran, editors, Offshore Wind Farms, pages 543-572. Woodhead Publishing, 2016. [33] Peter Tavner. Offshore Wind Turbines: Reliability, availability and maintenance. Energy Engineering. Institution of Engineering and Technology, 2012. [34] The True Digital Twin Concept for Fatigue Re-Assessment of Marine Structures, volume Volume 1: Offshore Technology of International Conference on Offshore Mechanics and Arctic Engineering, 2018. V001T01A021. [35] Dawid Augustyn, Martin D. Ulriksen, and John D. SÃ ̧rensen. Reliability updating of offshore wind substructures by use of digital twin information. Energies, 14(18), 2021. [36] Van Hoa Nguyen, Quoc Tuan Tran, Yvon Besanger, Marc Jung, and Tung Lam Nguyen. Digital twin integrated power-hardware-in-the-loop for the assessment of distributed renewable energy resources. Electrical Engineering, 104(2):377-388, 2022. [37] Xinya Song, Teng Jiang, Steffen Schlegel, and Dirk Westermann. Parameter tuning for dynamic digital twins in inverter-dominated distribution grid. IET Renewable Power Generation, 14(5):811-821, 2020. [38] Meisam Jahanshahi Zeitouni, Ahmad Parvaresh, Saber Abrazeh, Saeid-Reza Mohseni, Meysam Gheisarnejad, and Mohammad-Hassan Khooban. Digital twins-assisted design of next-generation advanced controllers for power systems and electronics: Wind turbine as a case study. Inventions, 5(2), 2020. [39] O. Oñederra, F. J. Asensio, P. Eguia, E. Perea, A. Pujana, and L. Martinez. Mv cable modeling for application in the digital twin of a windfarm. In 2019 International Conference on Clean Electrical Power (ICCEP), pages 617-622, 2019. [40] Makhsud Mansurovich Sultanov, Edik Koirunovich Arakelyan, Ilia Anatolevich Boldyrev, Valentina Sergeevna Lunenko, and Pavel Dmitrievich Menshikov. Digital twins application in control systems for distributed generation of heat and electric energy. Archives of Thermodynamics, 42(2), 2021. [41] Gopi Krishna Durbhaka and Barani Selvaraj. Convergence of artificial intelligence and internet of things in predictive maintenance systems-a review. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(11):205-214, 2021. [42] Tudor Cioara, Ionut Anghel, Marcel Antal, Ioan Salomie, Claudia Daniela Antal, and Arcas Gabriel Ioan. An overview of digital twins application domains in smart energy grid. ArXiv, 2104.07904, 2021. [43] You Lv, Carlos E. Romero, Tingting Yang, Fang Fang, and Jizhen Liu. Typical condition library construction for the development of data-driven models in power plants. Applied Thermal Engineering, 143:160-171, 2018. [44] Chunsheng Hu, Wenbo Shi, and Lekai Jiang. Application case of digital twin technology in electric power system. IOP Conference Series: Materials Science and Engineering, 788(1):012083, 2020.
dc.relation.ispartofjournal.eng.fl_str_mv	Transactions on Energy Systems and Engineering Applications
dc.relation.citationvolume.eng.fl_str_mv	3
dc.relation.citationstartpage.none.fl_str_mv	1
dc.relation.citationendpage.none.fl_str_mv	9
dc.relation.bitstream.none.fl_str_mv	https://revistas.utb.edu.co/tesea/article/download/484/369
dc.relation.citationedition.eng.fl_str_mv	Núm. 2 , Año 2022 : Transactions on Energy Systems and Engineering Applications
dc.relation.citationissue.eng.fl_str_mv	2
dc.rights.eng.fl_str_mv	Tek Tjing Lie, Leila Kamyabi, Samaneh Madanian - 2022
dc.rights.uri.eng.fl_str_mv	https://creativecommons.org/licenses/by/4.0
dc.rights.accessrights.eng.fl_str_mv	info:eu-repo/semantics/openAccess
dc.rights.creativecommons.eng.fl_str_mv	This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.rights.coar.eng.fl_str_mv	http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv	Tek Tjing Lie, Leila Kamyabi, Samaneh Madanian - 2022 https://creativecommons.org/licenses/by/4.0 This work is licensed under a Creative Commons Attribution 4.0 International License. http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.mimetype.eng.fl_str_mv	application/pdf
dc.publisher.eng.fl_str_mv	Universidad Tecnológica de Bolívar
dc.source.eng.fl_str_mv	https://revistas.utb.edu.co/tesea/article/view/484
institution	Universidad Tecnológica de Bolívar
repository.name.fl_str_mv	Repositorio Digital Universidad Tecnológica de Bolívar
repository.mail.fl_str_mv	bdigital@metabiblioteca.com
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spelling	Kamyabi, LeilaLie, Tek TjingMadanian, Samaneh2022-07-28 16:45:262025-05-21T19:15:45Z2022-07-28 16:45:262022-07-28https://hdl.handle.net/20.500.12585/13503https://doi.org/10.32397/tesea.vol3.n2.48410.32397/tesea.vol3.n2.4842745-0120Data science-based digital twin models of renewable energy system technologies developed in a real-time data-rich environment help develop better decisions and predictions than those in the present environment. Based on this real-time analysis of countrywide data, digital twin contributes to effective and reduced cost-based power system control at the localised level. Developing digital twin models from the collection of relevant data is an innovative technology. The challenge is how to leverage all the operational data and analyse the use of data from across transmission and distribution networks to help achieve the objectives. This paper presents an overview of the existing applications of digital twins in power systems.application/pdfengUniversidad Tecnológica de BolívarTek Tjing Lie, Leila Kamyabi, Samaneh Madanian - 2022https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.utb.edu.co/tesea/article/view/484Data SciencesDigital TwinPower Systemssolar PV and Wind Turbine GenerationApplications of Digital Twins in Power Systems: A PerspectiveApplications of Digital Twins in Power Systems: A PerspectiveArtículo de revistainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Journal articleTextinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Panayiotis Moutis and Omid Alizadeh-Mousavi. Digital twin of distribution power transformer for real-time monitoring of medium voltage from low voltage measurements. IEEE Transactions on Power Delivery, 36(4):1952-1963, 2021. [2] Independent Group of Scientists appointed by the Secretary-General. The Future is Now: Science for Achieving Sustainable Development (GSDR 2019). Technical report, United Nations, 2019. [3] Carlos Henrique dos Santos, José Arnaldo Barra Montevechi, José Antônio de Queiroz, Rafael de Carvalho Miranda, and Fabiano Leal. Decision support in productive processes through des and abs in the digital twin era: a systematic literature review. International Journal of Production Research, 60(8):2662-2681, 2022. [4] Anton Rassõlkin, Tamas Orosz, Galina Lvovna Demidova, Vladimir Kuts, Viktor Rjabtšikov, Toomas Vaimann, and Ants Kallaste. Implementation of digital twins for electrical energy conversion systems in selected case studies. Proceedings of the Estonian Academy of Sciences, 70(1):19 - 39, 2021. [5] Barbara Rita Barricelli, Elena Casiraghi, and Daniela Fogli. A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE Access, 7:167653-167671, 2019. [6] Aidan Fuller, Zhong Fan, Charles Day, and Chris Barlow. Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8:108952-108971, 2020. [7] Vivi Qiuchen Lu, Ajith Kumar Parlikad, Philip Woodall, Gishan Don Ranasinghe, and James Heaton. Developing a dynamic digital twin at a building level: Using cambridge campus as case study. In International conference on smart infrastructure and construction 2019 (ICSIC) driving data-informed decision-making, pages 67-75. ICE Publishing, 2019. [8] Greyce N. Schroeder, Charles Steinmetz, Carlos E. Pereira, and Danubia B. Espindola. Digital twin data modeling with automationml and a communication methodology for data exchange. IFAC-PapersOnLine, 49(30):12-17, 2016. 4th IFAC Symposium on Telematics Applications TA 2016. [9] Huaming Pan, Zhenlan Dou, Yanxing Cai, Wenzhu Li, Xing Lei, and Dong Han. Digital twin and its application in power system. In 2020 5th International Conference on Power and Renewable Energy (ICPRE), pages 21-26, 2020. [10] Ali Aghazadeh Ardebili, Antonella Longo, and Antonio Ficarella. Digital twin (dt) in smart energy systems-systematic literature review of dt as a growing solution for energy internet of the things (eiot). E3S Web of Conferences, 312:09002, 2021. [11] Wei Zhou. Research on wireless sensor network access control and load balancing in the industrial digital twin scenario. Journal of Sensors, 2022:3929958, 2022. [12] Xueyong Tang, Yi Ding, Jinyong Lei, He Yang, and Yankan Song. Dynamic load balancing method based on optimal complete matching of weighted bipartite graph for simulation tasks in multi-energy system digital twin applications. Energy Reports, 8:1423-1431, 2022. [13] Yasmin Fathy, Mona Jaber, and Zunaira Nadeem. Digital twin-driven decision making and planning for energy consumption. Journal of Sensor and Actuator Networks, 10(2), 2021. [14] Hyang-A Park, Gilsung Byeon, Wanbin Son, Hyung-Chul Jo, Jongyul Kim, and Sungshin Kim. Digital twin for operation of microgrid: Optimal scheduling in virtual space of digital twin. Energies, 13(20):5504, 2020. [15] A A Smirnov, V S Lunenko, and I A Boldyrev. Application of digital twins of equipment for managing complex renewable energy. IOP Conference Series: Materials Science and Engineering, 1035(1):012023, 2021. [16] Saran Ganesh, Arcadio Perilla, Jose Rueda Torres, Peter Palensky, and Mart van der Meijden. Validation of emt digital twin models for dynamic voltage performance assessment of 66 kv offshore transmission network. Applied Sciences, 11(1), 2021. [17] Palak Jain, Jason Poon, Jai Prakash Singh, Costas Spanos, Seth R. Sanders, and Sanjib Kumar Panda. A digital twin approach for fault diagnosis in distributed photovoltaic systems. IEEE Transactions on Power Electronics, 35(1):940-956, 2020. [18] Kamel Arafet and Rafael Berlanga. Digital twins in solar farms: An approach through time series and deep learning. Algorithms, 14(5), 2021. [19] Yuan He, Junchen Guo, and Xiaolong Zheng. From surveillance to digital twin: Challenges and recent advances of signal processing for industrial internet of things. IEEE Signal Processing Magazine, 35(5):120-129, 2018. [20] Xiang Xie, Ajith Kumar Parlikad, and Ramprakash Srinivasan Puri. A neural ordinary differential equations based approach for demand forecasting within power grid digital twins. In 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pages 1-6, 2019. [21] Edward O'Dwyer, Indranil Pan, Richard Charlesworth, Sarah Butler, and Nilay Shah. Integration of an energy management tool and digital twin for coordination and control of multi-vector smart energy systems. Sustainable Cities and Society, 62:102412, 2020. [22] Minglei You, Qian Wang, Hongjian Sun, Iván Castro, and Jing Jiang. Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties. Applied Energy, 305:117899, 2022. [23] Yan Xu. A review of cyber security risks of power systems: from static to dynamic false data attacks. Protection and Control of Modern Power Systems, 5(1):1-12, 2020. [24] Ahmed Saad, Samy Faddel, Tarek Youssef, and Osama A. Mohammed. On the implementation of iot-based digital twin for networked microgrids resiliency against cyber attacks. IEEE Transactions on Smart Grid, 11(6):5138-5150, 2020. [25] Fei Tao, Meng Zhang, Yushan Liu, and A.Y.C. Nee. Digital twin driven prognostics and health management for complex equipment. CIRP Annals, 67(1):169-172, 2018. [26] Krishnamoorthi Sivalingam, Marco Sepulveda, Mark Spring, and Peter Davies. A review and methodology development for remaining useful life prediction of offshore fixed and floating wind turbine power converter with digital twin technology perspective. In 2018 2nd International Conference on Green Energy and Applications (ICGEA), pages 197-204, 2018. [27] Hergen Pargmann, Dörthe Euhausen, and Robin Faber. Intelligent big data processing for wind farm monitoring and analysis based on cloud-technologies and digital twins: A quantitative approach. In 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), pages 233-237, 2018. [28] Yong Yang, Zhu Chen, Jing Yan, Zhi Xiong, Jun Zhang, Hongxia Yuan, Yali Tu, and Tianyun Zhang. State evaluation of power transformer based on digital twin. In 2019 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pages 230-235. IEEE, 2019. [29] Farid K. Moghadam and Amir R. Nejad. Online condition monitoring of floating wind turbines drivetrain by means of digital twin. Mechanical Systems and Signal Processing, 162:108087, 2022. [30] Amir Ebrahimi. Challenges of developing a digital twin model of renewable energy generators. In 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), pages 1059-1066, 2019. [31] ORACLE. Digital twins for iot applications a comprehensive approach to implementing iot digital twins. Technical report, ORACLE White Paper, 2017. [32] W. Yang. 18 - condition monitoring of offshore wind turbines. In Chong Ng and Li Ran, editors, Offshore Wind Farms, pages 543-572. Woodhead Publishing, 2016. [33] Peter Tavner. Offshore Wind Turbines: Reliability, availability and maintenance. Energy Engineering. Institution of Engineering and Technology, 2012. [34] The True Digital Twin Concept for Fatigue Re-Assessment of Marine Structures, volume Volume 1: Offshore Technology of International Conference on Offshore Mechanics and Arctic Engineering, 2018. V001T01A021. [35] Dawid Augustyn, Martin D. Ulriksen, and John D. SÃ ̧rensen. Reliability updating of offshore wind substructures by use of digital twin information. Energies, 14(18), 2021. [36] Van Hoa Nguyen, Quoc Tuan Tran, Yvon Besanger, Marc Jung, and Tung Lam Nguyen. Digital twin integrated power-hardware-in-the-loop for the assessment of distributed renewable energy resources. Electrical Engineering, 104(2):377-388, 2022. [37] Xinya Song, Teng Jiang, Steffen Schlegel, and Dirk Westermann. Parameter tuning for dynamic digital twins in inverter-dominated distribution grid. IET Renewable Power Generation, 14(5):811-821, 2020. [38] Meisam Jahanshahi Zeitouni, Ahmad Parvaresh, Saber Abrazeh, Saeid-Reza Mohseni, Meysam Gheisarnejad, and Mohammad-Hassan Khooban. Digital twins-assisted design of next-generation advanced controllers for power systems and electronics: Wind turbine as a case study. Inventions, 5(2), 2020. [39] O. Oñederra, F. J. Asensio, P. Eguia, E. Perea, A. Pujana, and L. Martinez. Mv cable modeling for application in the digital twin of a windfarm. In 2019 International Conference on Clean Electrical Power (ICCEP), pages 617-622, 2019. [40] Makhsud Mansurovich Sultanov, Edik Koirunovich Arakelyan, Ilia Anatolevich Boldyrev, Valentina Sergeevna Lunenko, and Pavel Dmitrievich Menshikov. Digital twins application in control systems for distributed generation of heat and electric energy. Archives of Thermodynamics, 42(2), 2021. [41] Gopi Krishna Durbhaka and Barani Selvaraj. Convergence of artificial intelligence and internet of things in predictive maintenance systems-a review. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(11):205-214, 2021. [42] Tudor Cioara, Ionut Anghel, Marcel Antal, Ioan Salomie, Claudia Daniela Antal, and Arcas Gabriel Ioan. An overview of digital twins application domains in smart energy grid. ArXiv, 2104.07904, 2021. [43] You Lv, Carlos E. Romero, Tingting Yang, Fang Fang, and Jizhen Liu. Typical condition library construction for the development of data-driven models in power plants. Applied Thermal Engineering, 143:160-171, 2018. [44] Chunsheng Hu, Wenbo Shi, and Lekai Jiang. Application case of digital twin technology in electric power system. IOP Conference Series: Materials Science and Engineering, 788(1):012083, 2020.Transactions on Energy Systems and Engineering Applications319https://revistas.utb.edu.co/tesea/article/download/484/369Núm. 2 , Año 2022 : Transactions on Energy Systems and Engineering Applications220.500.12585/13503oai:repositorio.utb.edu.co:20.500.12585/135032025-05-21 14:15:45.433https://creativecommons.org/licenses/by/4.0Tek Tjing Lie, Leila Kamyabi, Samaneh Madanian - 2022metadata.onlyhttps://repositorio.utb.edu.coRepositorio Digital Universidad Tecnológica de Bolívarbdigital@metabiblioteca.com

Applications of Digital Twins in Power Systems: A Perspective

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