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April 12, 2026IET Generation Transmission & Distribution0 citationsOpen Access

Robustness and Performance Analysis of a Current‐Controlled Quasi‐Stationary Electrical Model Virtual Synchronous Machine Using a Parameter‐Dependent Operating Point

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CZChristina ZuromskiFPFrancesco Giacomo PuricelliJBJef Beerten

Key Points

  • The central aim is to analyze the stability of current-controlled quasi-stationary electrical models under various grid conditions.
  • Robustness analysis of a grid-forming current-controlled virtual synchronous machine
  • Introduced a parameter-dependent operating point for eigenvalue computation
  • Employed sensitivity function analysis for performance evaluation
  • Control parameter selection significantly enhances robust stability under grid uncertainties
  • Neglecting operating point variations can mislead stability conclusions
  • Achieved efficient eigenvalue computation without repeated relinearization

Abstract

ABSTRACT Ensuring stability in converter‐dominated power systems requires voltage source converters to be robust under varying grid conditions and grid uncertainties such as short‐circuit ratio variations. This paper analyzes the robustness of a grid‐forming (GFM) current‐controlled quasi‐stationary electrical model virtual synchronous machine. We investigate the applicability of ‐analysis to systems incorporating GFM converters and demonstrate that operating point variations induced by parameter uncertainties must be explicitly considered. Due to the system's inherent nonlinearity, neglecting these variations can lead to misleading stability conclusions. To address the limitations of the ‐analysis, we introduce a robustness analysis method based on a parameter‐dependent operating point and symbolic linearization, which enables efficient eigenvalue computation without repeated relinearization. Performance is additionally evaluated using sensitivity function analysis with respect to power reference tracking and angle disturbance rejection. The results show that appropriate control parameter selection improves robust stability under grid uncertainties by reducing undesired interactions.

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Cite This Study

Zuromski et al. (2026) studied this question.

synapsesocial.com/papers/69db37df4fe01fead37c5feehttps://doi.org/10.1049/gtd2.70291
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