The extensive integration of distributed generation complicates power system operation, challenging the computational efficiency and scalability of existing analytical tools. To address this, efficient reduced-order modeling techniques must be developed for representing multi-inverter networks. A promising approach involves coherency-based aggregation to create simplified dynamic models for large-scale distributed generation systems with heterogeneous inverters. Simple and efficient, conventional methods retain network nonlinearities, limiting accuracy in dynamic analyses under disturbances. And prior coherency analyses often overlook virtual EMFs (Electromotive Forces) and parameter coupling effects among inverters. To resolve these limitations, a parameter weight-based coherency equivalence method for VSGs ( Virtual Synchronous Generators) is proposed. First,through small-signal analysis, the voltage-current dual-loop control of inverters is shown to exert minimal influence on virtual rotor motion, enabling the derivation of a virtual synchronous generator excitation mode grounded in physical principles. This achieves precise coherency identification under small perturbations while clarifying the parametric influence mechanisms. Subsequently, virtual rotor motion equations are utilized to quantitatively analyze parameter impacts (e. g. , moment of inertia J, damping coefficient D, and line reactance X), with results assigned as parameter weights to establish a weighted coherency algorithm. This algorithm enables rapid and accurate identification of coherent inverter clusters, followed by an aggregation algorithm that integrates active power conservation and reactive power dynamic equivalence. The correctness and effectiveness of the proposed coherency and aggregation methods are validated via PLECS simulations.

coherency equivalence algorithm, parameter weight, weighted clustering, virtual synchronous generator(VSG), dynamic aggregation