## 1 Introduction
[19] proposes a simplification algorithm based on equivalent circuit modeling for large-scale battery packs, which can significantly improve simulation speed while maintaining accuracy, considering inconsistencies. However, most of the above studies have simplified or abstracted the models for different problems modeled in a particular scenario, whichfacilitates the analysis of the problem but, at the same time, reduces the accuracy of the model. In addition, existing studies also lack in-depth research on the modeling methods of new energy systems that take into account different fault control characteristics. In reality, when different types of short-circuit faults occur in the power grid, the new energy units tend to exhibit different control characteristics, i.e., the control objectives, control structures, control parameters, and control instructions of the unit control system will change with the fault changes to make the units exhibit different dynamic response characteristics [20; 21]. In terms of model control parameter calibration, the transient control structure and parameters of the inverter, as the core component of the new energy power generation system, determine the transient response characteristics of the new energy during fault. However, the control structure and the parameters of the inverter in the new energy unit are usually complex to obtain directly due to changes in the operating environment and the black box nature of the actual controller. Therefore, it is necessary to estimate the unknown links in the model by combining the system identification method. System identification encompasses two key aspects: system structure identification and system parameter identification. For the system model structure, this paper selects a general model that can satisfy various calculation functions, such as tidal current calculation, transient stability simulation calculation, short-circuit calculation, and small disturbance stability calculation, among others. This model also features a variety of different decoupling control strategies. The parameter identification of the system is primarily divided into traditional identification methods and artificial intelligence identification methods [22]. Among them, the traditional identification method obtains the control parameters of the model through dynamic fitting methods, including the least squares method, theoretical analysis method, and frequency domain analysis method, among others. [PERSON] et al. [23] proposes a two-stage parameter identification method for a direct-drive wind turbine converter dual closed-loop control system. A power step command is used to identify the control parameters based on the current response, employing the damped least squares method. [PERSON] et al. [24] proposes a recursive least squares sensorless identification method for online identification of permanent magnet synchronous generator parameters, which can effectively detect feature changes during aging and degradation. [PERSON] et al. [25] proposed a new decoupled weighted recursive least squares (DWRLS) method, which improves the modeling accuracy by estimating fast and slow dynamic parameters separately. [PERSON] et al. [26] improves the parameter discrimination accuracy of state-of-charge (SOC) estimation models for battery management systems based on the forgetting factor recursive least squares method. The traditional identification methods are generally only applicable to the identification of linear systems, and they are difficult to apply to the identification of nonlinear systems. To address this issue, artificial intelligence (AI)-based identification methods have been proposed in the literature. These AI methods are intelligent techniques to identify model parameters by simulating human intelligence, automatic reasoning, learning, and searching, with strong problem-solving ability, and are not subject to the constraints of nonlinear links.