## 5 Simulation Example Analysis
### Verify Symmetric Fault Identification Results  
In this section, the fault conditions with a symmetrical voltage drop to 0%, 20%, 35%, 50%, and 75% under low and medium power conditions are used as the training set for symmetrical low-penetration control identification of the structured model, and the fault conditions with a symmetrical voltage drop to 50% under high power conditions are used as the test set for the symmetrical low-penetration control identification results. Similarly, the fault conditions where the symmetrical voltage rises to 120% and 130% under low and medium power conditions are used as the training set for symmetrical high penetration control identification, and the fault condition where the symmetrical voltage rises to 125% under high power conditions is used as the test set for symmetrical high penetration control identification results. In the identification process, the common normal equation analytical method NEAM (normal equation analytical method), particle swarm algorithm PSO (particle swarm optimization), and simulated annealing algorithm SA (Simulated Annealing) are used as the comparison algorithms with the proposed algorithm in this paper. The genetic quasi-Newton algorithm (GQNA) is used as a comparison algorithm for control identification. The results of different algorithms' control parameter identification under symmetric low-voltage ride-through and high-voltage ride-through inspection conditions are shown in Tables 6 and 7, respectively; the comparison between simulation data and measured data of different algorithms' control identification under symmetric low-voltage ride-through inspection conditions is shown in Figure 17a, and the comparison between simulation data and measured data of different algorithms' control identification under symmetric high-voltage ride-through inspection conditions is shown in Figure 17b; The deviations of the different algorithms' recognition simulation results under symmetrical LVRT and HVRT test conditions are compared in Figures 18 and 19; the steady-state average deviations of the different algorithms' control recognition simulation results during fault ride-through under symmetrical LVRT and HVRT test conditions are shown in Tables 8 and 9, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **NEAM** & **PSO** & **SA** & **GQNA** \\ \hline K\({}_{\text{vp}}\) & 0.4526 & 0.4472 & 0.0092 & 0.4369 \\ K\({}_{\text{id}}\) & 0.0619 & 0.0574 & \(-\)0.0921 & 0.0588 \\ I\({}_{\text{dset}}\) & 0.4357 & 0.4283 & 0.7669 & 0.4071 \\ K\({}_{\text{vq}}\) & 2.1054 & 2.282 & 1.9134 & 1.9132 \\ K\({}_{\text{iq}}\) & \(-\)1.3573 & \(-\)0.9428 & 30.5741 & \(-\)1.0948 \\ I\({}_{\text{qset}}\) & 0.0584 & 0.0701 & 0.1338 & 0.063 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Recognition results of different algorithms for symmetrical LVRT conditions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **NEAM** & **PSO** & **SA** & **GQNA** \\ \hline K\({}_{\text{vp}}\) & \(-0.5361\) & \(-0.0742\) & \(0.0175\) & \(-0.515\) \\ K\({}_{\text{id}}\) & \(0.8368\) & \(0.8987\) & \(0.9102\) & \(0.8675\) \\ I\({}_{\text{dset}}\) & \(0.595\) & \(0.052\) & \(-0.0583\) & \(0.5649\) \\ K\({}_{\text{vq}}\) & \(2.213\) & \(2.3625\) & \(0.0968\) & \(2.4393\) \\ K\({}_{\text{id}}\) & \(0.1936\) & \(0.1759\) & \(7.7392\) & \(0.1778\) \\ I\({}_{\text{qset}}\) & \(-0.0019\) & \(-0.0105\) & \(-0.0997\) & \(0.0719\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Recognition results of different algorithms for symmetrical HVRT conditions. Figure 17: _Cont._Figure 17: Comparison of the high-power symmetrical LVRT and HVRT simulation and measured data: (**a**) symmetrical LVRT voltage drops to 50%; (**b**) symmetrical HVRT voltage rises to 125%. Figure 18: Comparison of deviation calculation results of different algorithms for high-power symmetric LVRT conditions.