Analysis of the safe operation region of grid-connected converters under time-varying operating point conditions

Plenty of engineering operation experience indicates that the performance of grid-connected converters (GCCs) undergoes a transformation in response to alterations in operating points. The operating points of the GCCs are closely related to several factors such as the volatility of new energy and the random switching of loads, therefore exhibit time-varying characteristics. While traditional impedance/admittance analysis methods are effective in analysing the stability of GCCs at fixed operating points, they become inefficient for systems with time-varying conditions due to the requirement of repeat analyses across multiple operating points. This process becomes particularly time-consuming for systems with numerous potential operating points, ultimately failing to capture the system’s behavior over its entire operating range. Therefore, the current research gap lies in the inability to effectively and comprehensively analyse GCC stability under time-varying conditions, which is a common feature of highly renewable energy integrated systems. This gap is critical because such fluctuations are highly relevant to renewable energy generation systems and can significant impact on GCC stability, and the lack of effective analysis methods may lead to underperform or even failure of these systems. To address this, this paper proposes a unified SISO model that embeded operating point variables directly into the small-signal analysis of GCCs. This approach enables stability analysis across the entire operating range of GCCs without redundant evaluations. Additionally, the proposed method integrates the advantages of the multivariate approach with an intuitive representation of stability based on the safe operating region (SOR), offering an efficient, accurate, and practical method for assessing stability in realistic time-varying environments, which has been challenging in previous research. A 1.7 kW/60 V experimental prototype is constructed to experimentally validate the accuracy and effectiveness of the proposed SOR-based method under both static and time-varying operating conditions.