JOURNAL ARTICLE

Control tuning methodology for modular multilevel converter‐based STATCOM.

  • Published In: International Journal of Circuit Theory & Applications, 2024, v. 52, n. 5. P. 2493 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Sousa, Renata O.; Cupertino, Allan; Pinto, Jonathan H. D. G.; Morais, Lenin M. F.; Pereira, Heverton A.; Teodorescu, Remus 3 of 3

Abstract

Summary: The application of modular multilevel converters (MMC) in medium voltage static synchronous compensators (STATCOMs) has been investigated in recent years. A huge challenge for MMC‐STATCOM is to compute the gain of the controllers. Therefore, the contribution of this paper is the proposal of a control tuning methodology for MMC‐STATCOM. For this purpose, the control and tuning strategies are presented. Since MMC‐STATCOM can employ different modulation strategies, two distinct modulation strategies are considered: nearest‐level control, with variable switching frequency; and phase‐shifted carrier pulse‐width modulation, with fixed switching frequency. Moreover, the proposal was validated through simulation and an experimental platform. The results indicate that the power response follows the reference, besides supplying the internal losses of the converter. The 10% ripple tolerance of the submodule voltages is respected in steady state for both modulation strategies. Moreover, the 5% total demand distortion (TDD) limit recommended by the Institute of Electrical and Electronics Engineers (IEEE) standard is attended. These results demonstrate the effectiveness of the controls employed. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Circuit Theory & Applications. 2024/05, Vol. 52, Issue 5, p2493
  • Document Type:Article
  • Subject Area:Engineering
  • Publication Date:2024
  • ISSN:0098-9886
  • DOI:10.1002/cta.3878
  • Accession Number:176846312
  • Copyright Statement:Copyright of International Journal of Circuit Theory & Applications is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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