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Cited by 34Microgrid Control and OptimizationReal-time simulation and control systemsPower System Optimization and Stability

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Data-Driven Modeling of Grid-Forming Inverter Dynamics Using Power Hardware-in-the-Loop Experimentation

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Guruwacharya, Nischal, Chakraborty, Soham, Saraswat, Govind, Bryce, Richard, Hansen, Timothy M., Tonkoski, Reinaldo·2024·IEEE Access
DOI 10.1109/access.2024.3383323
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Abstract

Recently, there is rapid integration of power electronic converter (PECs) into the power grid. Most of these PECs are grid-following inverters, where weak grid operation becomes an issue. Research is now shifting focus to grid-forming (GFM) inverters, resembling synchronous generators. The shift towards converter-based generation necessitates accurate PEC models for assessing system dynamics that were previously ignored in conventional power systems. Data-driven modeling (DDM) techniques are becoming valuable tools for capturing the dynamic behavior of advanced control strategies for PECs. This paper proposes using power hardware-in-the-loop experiments to capture dynamic GFM data in the application of DDM techniques. Furthermore, the paper derives an analytical approach to obtaining a mathematical model of GFM inverter dynamics and compares it with the DDM. A square-chirp probing signal was employed to perturb the active and reactive power of the load inside an Opal-RT model. The dynamic response of the GFM inverter, including changes in frequency and voltage, was recorded. This data was then used in a system identification algorithm to derive the GFM DDMs. The effectiveness of DDM is cross-validated with an analytical approach through experimental simulation studies, and the goodness-of-fit for both approaches is compared. Both approaches show more than 85% accuracy in capturing the dynamic response of GFM inverters under different loading conditions.

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References 46 · Cited by 34

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  1. 10.1109/ACCESS.2017.2741578 · DOI
  2. 10.1109/PESC.2004.1355281 · DOI
  3. 10.1109/ACCESS.2021.3086420 · DOI
  4. Kundur, 1994, Power System Stability and Control, 7
  5. 10.23919/PSCC.2018.8450880 · DOI
  6. 2023, Inverter-Based Resource Performance Issues Report
  7. 10.1109/OJIA.2020.3020392 · DOI
  8. 10.1109/63.844502 · DOI
  9. 10.1109/TPEL.2020.3015158 · DOI
  10. 10.1109/TSG.2013.2288000 · DOI
  11. 10.1109/OJIA.2021.3074028 · DOI
  12. 10.1109/APPEEC53445.2022.10072251 · DOI
  13. 10.1109/MPEL.2016.2614906 · DOI
  14. 10.1109/TPEL.2022.3163377 · DOI
  15. 10.1109/TEC.2013.2263885 · DOI
  16. 10.1109/TPEL.2015.2398192 · DOI
  17. 10.1109/TIE.2014.2334665 · DOI
  18. 10.1109/MPE.2019.2933072 · DOI
  19. 10.1016/j.prime.2023.100365 · DOI
  20. 10.3390/en12060989 · DOI
  21. 10.1109/ieeestd.2018.8332112 · DOI
  22. 10.1109/ISGT49243.2021.9372183 · DOI
  23. 10.1155/2018/5280681 · DOI
  24. 10.1109/APEC.2010.5433336 · DOI
  25. 10.1109/PESGM41954.2020.9281783 · DOI
  26. 10.1109/SPEEDAM53979.2022.9842001 · DOI
  27. 10.1109/PESGM.2015.7286302 · DOI
  28. 10.1109/PESC.1976.7072895 · DOI
  29. 10.1109/ACCESS.2021.3090320 · DOI
  30. 10.1049/iet-epa:20060276 · DOI
  31. 10.1109/TIE.2011.2167730 · DOI
  32. 10.1109/81.735437 · DOI
  33. 10.1002/047134608X.W1046 · DOI
  34. 2024, System Identification Toolbox
  35. 10.1109/TPEL.2007.900551 · DOI
  36. 10.1109/TSG.2017.2707345 · DOI
  37. 10.1109/PESC.2008.4592063 · DOI
  38. 10.1002/9780470551578 · DOI
  39. 10.1109/JESTPE.2014.2338131 · DOI
  40. 10.1007/978-3-662-04323-3 · DOI
  41. 10.1007/978-3-030-03661-4 · DOI
  42. 10.1109/NAPS50074.2021.9449811 · DOI
  43. 2024, Time Domain Specifications
  44. 10.1109/TIM.2009.2031836 · DOI
  45. Chakraborty, 2022, A review of active probing-based system identification techniques with applications in power systems, Int. J. Electr. Power Energy Syst., 140, 10.1016/j.ijepes.2022.108008 · DOI
  46. 10.1109/IECON48115.2021.9589066 · DOI
  47. 2024, Dynapower Powering a Cleaner Planet
  48. 10.1109/IECON49645.2022.9968638 · DOI

Metadata

Authors
Guruwacharya, N., Chakraborty, S., Saraswat, G., Bryce, R., Hansen, T. M., & Tonkoski, R.
Affiliations
  • National Laboratory of the Rockies (US)
  • University of Minnesota (US)
  • Enphase Energy (United States) (US)
  • South Dakota State University (US)
  • Technical University of Munich (DE)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2024
Volume
12
Pages
52267-52281
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2024.3383323
OpenAlex
W4393305376
Cited by
34
References
46
Citations by year
2026: 13 · 2025: 17 · 2024: 4
FWCI
5.32
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders
  • U.S. Department of Energy (DE-SC0020281)
  • National Science Foundation (OIA-2316399)
  • Advanced Research Projects Agency - Energy (DE-AR0001016)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Microgrid Control and Optimization
  • Physical Sciences › Engineering › Control and Systems Engineering › Real-time simulation and control systems
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
Keywords
grid-forming inverters, data-driven modeling, power hardware-in-the-loop, system identification, power converters, inverter dynamics, converter-based generation

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CoChat. (2026). Citation check: Data-Driven Modeling of Grid-Forming Inverter Dynamics Using Power Hardware-in-the-Loop Experimentation. https://app.cochat.ai/papers/10.1109/access.2024.3383323
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Guruwacharya, N., Chakraborty, S., Saraswat, G., Bryce, R., Hansen, T. M., & Tonkoski, R. (2024). Data-Driven Modeling of Grid-Forming Inverter Dynamics Using Power Hardware-in-the-Loop Experimentation. IEEE Access. https://doi.org/10.1109/access.2024.3383323