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Comparative Evaluation of Converter Control Impact on Torsional Dynamics of Type-IV Grid-Forming Wind Turbines
Aalborg Univ, DNV R&D Energy Syst, DK-9220 Aalborg, Denmark.;Aalborg Univ, Dept Energy AAU Energy, DK-9220 Aalborg, Denmark..
Aalborg Univ, Comp Sci Dept, DK-9220 Aalborg, Denmark..
Aalborg Univ, Dept Energy, AAU Energy, DK-9220 Aalborg, Denmark..
DNV R&D Energy Syst, NL-6812 AR Arnhem, Netherlands..
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2024 (English)In: IEEE Transactions on Sustainable Energy, ISSN 1949-3029, E-ISSN 1949-3037, Vol. 15, no 4, p. 2803-2814Article in journal (Refereed) Published
Abstract [en]

This paper explores the impact of back-to-back converter control strategies on the torsional dynamics of grid-forming permanent magnet synchronous generator wind turbines (GFM-WTs). Two general converter control methods for GFM-WTs, distinguished by the placement of dc-link voltage control (DVC)-either in the machine-side converter or the grid-side converter, are evaluated through the complex torque coefficient method to offer a theoretical basis. It reveals that a negative damping is introduced when the DVC is with the machine-side converter. Then, to further characterize the parametric impacts of electrical and mechanical system constants and controller gains, the sensitivity analysis is performed by employing the partial derivative algorithm, which is based on the feedforward neural network training. It is found that the converter control has a limited impact on the damped frequency in both GFM converter control methods. Finally, electromagnetic simulations based on full-order nonlinear models of GFM-WTs are carried out to confirm the theoretical findings.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2024. Vol. 15, no 4, p. 2803-2814
Keywords [en]
Voltage control, Wind turbines, Grid forming, Torque, Permanent magnets, Frequency control, Vibrations, Power grids, Synchronous generators, Feedforward neural networks, Grid-forming, permanent magnet synchronous generator, torsional dynamics, feedforward neural network
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-355153DOI: 10.1109/TSTE.2024.3444474ISI: 001328315300040Scopus ID: 2-s2.0-85202751034OAI: oai:DiVA.org:kth-355153DiVA, id: diva2:1907986
Note

QC 20241024

Available from: 2024-10-24 Created: 2024-10-24 Last updated: 2024-10-24Bibliographically approved

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Wang, Xiongfei

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