## 6 Conclusions  
In this paper, we propose a non-intrusive parameter identification method based on dynamic trajectory feature extraction and a hybrid optimization algorithm to address the challenges of identifying HVRT/LVRT control parameters for energy storage systems. The main conclusions can be summarized as follows:  
1. Dynamic Trajectory Feature Extraction Mechanism: By capturing key features corresponding to different dynamic responses, a sensitivity mapping relationship is established between control parameters and dynamic responses. This approach significantly improves the feature matching accuracy under HVRT/LVRT multi-scenario conditions and reduces identification deviations caused by parameter coupling effects. 2. By integrating global search with local refinement, this strategy addresses the issue of local optima in high-dimensional nonlinear parameter spaces. The simulation results \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Interval** & \(\mathbf{\Delta U}_{mean}\) & \(\mathbf{\Delta P}_{mean}\) & \(\mathbf{\Delta Q}_{mean}\) & \(\mathbf{\Delta I}_{mean}\) & \(\mathbf{\Delta I}_{Qmean}\) \\ \hline Pre-fault steady-state & 0.25\% & 0.33\% & 0.25\% & 0.61\% & 0.21\% \\ Transient during fault & 0.82\% & 1.04\% & 0.54\% & 1.01\% & 0.84\% \\ Steady-state during fault & 1.51\% & 0.72\% & 1.09\% & 1.11\% & 1.01\% \\ Post-fault transient & 1.09\% & 0.92\% & 1.02\% & 1.16\% & 0.52\% \\ Post-fault steady-state & 0.30\% & 0.28\% & 0.24\% & 0.69\% & 0.22\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of steady-state average error in fault identification by the PSO-LM algorithm identification group under asymmetrical conditions (CH FP125%Un). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Interval** & \(\mathbf{\Delta U}_{mean}\) & \(\mathbf{\Delta P}_{mean}\) & \(\mathbf{\Delta Q}_{mean}\) & \(\mathbf{\Delta I}_{Pmean}\) & \(\mathbf{\Delta I}_{Qmean}\) \\ \hline Pre-fault steady-state & 0.85\% & 0.50\% & 0.75\% & 0.91\% & 0.71\% \\ Transient during fault & 1.02\% & 1.64\% & 1.54\% & 2.31\% & 2.24\% \\ Steady-state during fault & 1.11\% & 0.53\% & 1.69\% & 2.41\% & 0.88\% \\ Post-fault transient & 1.09\% & 1.59\% & 1.22\% & 1.76\% & 1.52\% \\ Post-fault steady-state & 0.90\% & 0.33\% & 0.74\% & 1.29\% & 0.72\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of steady-state average error in fault identification by the PSO-LM algorithm identification group under asymmetrical conditions (DH FP125%Un). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Interval** & \(\mathbf{\Delta U}_{mean}\) & \(\mathbf{\Delta P}_{mean}\) & \(\mathbf{\Delta Q}_{mean}\) & \(\mathbf{\Delta I}_{Pmean}\) & \(\mathbf{\Delta I}_{Qmean}\) \\ \hline Pre-fault steady-state & 0.26\% & 0.21\% & 0.23\% & 0.52\% & 0.31\% \\ Transient during fault & 1.07\% & 1.06\% & 1.72\% & 1.13\% & 1.54\% \\ Steady-state during fault & 1.17\% & 1.03\% & 2.14\% & 1.91\% & 2.97\% \\ Post-fault transient & 1.02\% & 1.11\% & 2.54\% & 1.31\% & 1.74\% \\ Post-fault steady-state & 0.30\% & 0.31\% & 0.62\% & 0.51\% & 0.32\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of steady-state average error in fault identification by the PSO-LM algorithm identification group under asymmetrical conditions (CH FP20%Un). demonstrate that the method achieves parameter identification errors of less than 3.5% across five typical voltage fault scenarios, exhibiting faster convergence and a strong generalization ability across different operating conditions. * Measurement-Driven Simulation Validation System: The method was validated through the fault ride-through model developed on the BPA platform, exhibiting key dynamic response metrics (e.g., voltage recovery time and reactive power support accuracy) with errors below 5% compared to field measurements, providing a high-fidelity simulation tool for transient stability assessment in renewable-integrated power systems. Future research directions will focus on the following: 1.