Grid-forming (GFM) converters are regarded as the most promising solution for grid-connected converters of renewable energy due to their robustness against weak grids. However, attributable to their voltage source characteristics, GFM converters may experience overcurrent issues during large disturbances. The virtual impedance (VI) method is an effective method to solve this problem. Nevertheless, there exists a contradiction between the demands for VI posed by current limitations and transient stability. Firstly, the analytical equations of virtual impedance for limiting the fault steady-state current of a GFM converter with different depths of grid voltage sag are solved. On that basis, an adaptive method based on virtual impedance is proposed to limit the fault current. Moreover, the analytical equation of the output active power of the converter when using adaptive virtual impedance to limit the fault current is solved. On this basis, the effect of the virtual impedance ratio on the transient stability is investigated. Finally, an adaptive virtual-impedance-based current-limiting method with the functionality of transient stability enhancement for a grid-forming converter is innovatively proposed. The enhancement effect of the method is verified by the equal area criterion method and phase portrait method. Finally, the efficacy of the proposed method in limiting fault currents and enhancing transient stability is validated through hardware-in-the-loop (HIL) experiments.

Accurate model aggregation is pivotal for the efficient dispatch and control of massive distributed photovoltaic (PV) and energy storage (ES) resources. However, the lack of unified standards across equipment manufacturers results in inconsistent data formats and resolutions. Furthermore, external disturbances like noise and packet loss exacerbate the problem. The resulting data are massive, multi-source, and heterogeneous, which poses severe challenges to building effective aggregation models. To address these issues, this paper proposes a hierarchical aggregation method based on multi-source multi-scale data fusion. First, a Multi-source Multi-scale Decision Table (Ms-MsDT) model is constructed to establish a unified framework for the flexible storage and representation of heterogeneous PV-ES data. Subsequently, a two-stage fusion framework is developed, combining Information Gain (IG) for global coarse screening and Scale-based Trees (SbT) for local fine-grained selection. This approach achieves adaptive scale optimization, effectively balancing data volume reduction with high-fidelity feature preservation. Finally, a hierarchical aggregation mechanism is introduced, employing the Analytic Hierarchy Process (AHP) and a weight-guided improved K-Means algorithm to perform targeted clustering tailored to the specific control requirements of different voltage levels. Validation on an IEEE-33 node system demonstrates that the proposed method significantly improves data approximation precision and clustering compactness compared to conventional approaches.