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Disturbance Observer-Based Model Predictive Power Synchronization Control for Suppression of Synchronous Oscillation
College of Electrical Engineering, Zhejiang University, Hangzhou, China.
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0003-0746-0221
College of Electrical Engineering, Zhejiang University, Hangzhou, China.
2023 (English)In: IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society, Institute of Electrical and Electronics Engineers (IEEE) , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Grid-forming (GFM) converters can achieve self-synchronization oriented by the active power balance, which is a promising solution for the high penetration of power electronics. Unfortunately, GFM control suffers from the synchronous oscillation (SO) issue, which may result in system instability. This paper proposes a disturbance observer-based model predictive power synchronization approach to suppress SOs of GFM converters. The mechanism of SO is investigated by the small-signal model of the grid-tied GFM converter, and it is revealed that the SO is induced by the electromagnetic dynamics of the power transfer in the transmission line and the power synchronization dynamics dominate this issue. Then, a model predictive power synchronization controller is proposed for mitigating SOs. In addition, a disturbance observer is developed to compensate the influence of disturbances/uncertainties in the system to improve the performance of power tracking. The proposed control approach is verified by simulations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2023.
Keywords [en]
Grid-forming converter, model predictive control, synchronous oscillation
National Category
Control Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-341624DOI: 10.1109/IECON51785.2023.10312124Scopus ID: 2-s2.0-85179510741OAI: oai:DiVA.org:kth-341624DiVA, id: diva2:1822826
Conference
49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023, Singapore, Singapore, Oct 16 2023 - Oct 19 2023
Note

Part of ISBN 9798350331820

QC 20231228

Available from: 2023-12-28 Created: 2023-12-28 Last updated: 2023-12-28Bibliographically approved

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Zhang, Mengfan

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • de-DE
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Output format
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