Data-Driven Impedance Model Reduction for Grid-Connected Converters

With the increasing integration of grid-connected converters, stability issues have become a significant concern. Impedance modeling has emerged as a promising solution for identifying stability problems. However, as the penetration of power electronics converters grows, the complexity of these models increases, making them difficult to manage. Existing studies primarily focus on model reduction for specific scenarios or operating points, which limits their applicability. In practical networks with a large number of converters, the computational burden remains high, rendering model reduction and analysis impractical. To address these challenges, this paper proposes a data-driven impedance model reduction method for grid-connected converters. Unlike traditional approaches, this method offers a more unified framework that is applicable to converters with diverse control structures and operating scenarios, while maintaining high accuracy. The effectiveness of the proposed method is validated through comprehensive simulation and experimental results.