## 5 Simulation Example Analysis
### Verify Symmetric Fault Identification Results
Comparative analysis of Figures 17a,b, 18 and 19, and Tables 8 and 9 shows that, in the control parameter identification of the structured model of the black box unit using the large-scale and noisy measured data set, the conventional regular equation analytic method (NEAM) can compute the approximate fitted optimal solution, but the average deviation values of the active and reactive steady state during the high and low fault ride-through periods are very close to the standard deviation, with a symmetrical low-voltage ride-through. The fault steady-state average deviations of active and reactive power during the high-voltage ride-through test condition are 0.0433, 0.0448 and 0.0444, 0.0353, respectively. When a single intelligent optimization algorithm, PSO or SA, is selected for parameter identification, the average deviation of the LVRT and HVRT reactive power of the inspection group is 0.0823, 0.0657, and 0.071, 0.1748, whose average errors are larger than the standard error deviation, and the simulation results fail to accurately characterize the HVRT and LVRT operation of the actual WTGs. However, the proposed algorithm in this paper reduces the average deviation of active power and reactive power during faults in symmetrical low-voltage ride-through and high-voltage ride-through test conditions by 0.0291, 0.0281, 0.0311, and 0.0143, respectively, compared to NEAM, and the average  
Figure 19: Comparison of deviation calculation results of different algorithms for high-power symmetric HVRT conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **U** & **P** & **Q** & **Id** & **Iq** \\ \hline NEAM & 0.0117 & 0.0433 & 0.0448 & 0.0365 & 0.0471 \\ PSO & 0.0117 & 0.0333 & 0.0823 & 0.0322 & 0.1008 \\ SA & 0.0117 & 0.0636 & 0.0657 & 0.0769 & 0.0769 \\ GQNA & 0.0117 & 0.0142 & 0.0167 & 0.0201 & 0.0217 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of steady-state mean deviation during fault ride-through with different algorithms under high-power symmetrical 50% \(\mathrm{U_{N}}\) LVRT. deviations of the grid-point voltage, active power, reactive power, active current, and reactive current are all within the standard deviation range. Comparison results show that the GQNA algorithm proposed in this paper exhibits significant advantages in fault ride-through control parameter identification for structured modeling of black box new energy units. Compared with the traditional NEAM method, GQNA can effectively overcome the problem of insufficient parameter identification accuracy in large-scale noisy data and multiple covariance environments; at the same time, compared with single intelligent optimization algorithms (e.g., PSO and SA), GQNA avoids the shortcoming of falling into the local optimal solution and significantly improves the robustness and accuracy of parameter identification. In addition, the simulation results further verify the effectiveness of the GQNA algorithm in the structured model fault ride-through control of the black box new energy unit, and its identification results can accurately reflect the dynamic characteristics of the system, demonstrating good engineering practicability and reliability and providing a new solution for the parameter identification of the complex black box new energy unit system.