## 5 Simulation Example Analysis
### Comparison of different algorithms for error calculation under asymmetric high penetration  
Analyzing Figures 20a,b, 21 and 22, and Tables 12 and 13, it can be seen that the GQNA algorithm proposed in this paper also performs well in the recognition of asymmetrical high- and low-voltage ride-through fault conditions. Similar to the results of steady-state average deviation analysis during fault ride-through in symmetrical conditions in Section 5.2, the overall identification simulation data of the GQNA algorithm in asymmetrical high-low-voltage ride-through fault identification is closer to the measured data of the working conditions and still has good stability and correctness. Specifically, its steady-state average deviation during active power and reactive power faults under high-power asymmetrical low-voltage ride-through and high-voltage ride-through test conditions are 0.0041, 0.0094, and 0.0072, 0.0058, respectively. Compared with the NEAM resolution method with superior identification performance, the proposed algorithm in this paper reduces the steady-state average deviation during active and reactive power faults by 0.0406, 0.0174, and 0.0213, 0.049 for low-voltage ride-through and high-voltage ride-through test conditions, respectively, which significantly improves the identification  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **U** & **P** & **Q** & **Id** & **Iq** \\ \hline NEAM & 0.0153 & 0.0285 & 0.0548 & 0.0333 & 0.0455 \\ PSO & 0.0153 & 0.0329 & 0.0444 & 0.0297 & 0.0452 \\ SA & 0.0153 & 0.0465 & 0.0854 & 0.0487 & 0.0785 \\ GQNA & 0.0153 & 0.0072 & 0.0058 & 0.0103 & 0.0044 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of steady-state mean deviation during fault ride-through with different algorithms under high-power asymmetrical 125% U\({}_{\text{N}}\) HVRT.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **U** & **P** & **Q** & **Id** & **Iq** \\ \hline NEAM & 0.0023 & 0.0447 & 0.0268 & 0.0566 & 0.0184 \\ PSO & 0.0023 & 0.0792 & 0.0314 & 0.0885 & 0.024 \\ SA & 0.0023 & 0.0244 & 0.0501 & 0.028 & 0.0751 \\ GQNA & 0.0023 & 0.0041 & 0.0094 & 0.0032 & 0.0027 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison of steady-state mean deviation during fault ride-through with different algorithms under high-power asymmetrical 50% U\({}_{\text{N}}\) LVRT.  
Figure 22: Comparison of deviation calculation results of different algorithms for high-power asymmetric HVRT conditions.  
accuracy. In order to comprehensively evaluate the overall performance enhancement of the algorithm in this paper, we compare and analyze the performance of the proposed algorithm with the traditional NEAM algorithm under four test conditions (symmetric LVRT, asymmetric LVRT, symmetric HVRT, and asymmetric HVRT). By counting the steady-state average deviation reduction values of active and reactive power during the fault period and calculating the arithmetic average of all four sets of deviation reduction data, the average error reduction rate of this paper's algorithm compared to the traditional method is 2.89%, which reflects the overall performance improvement of this paper's algorithm under different fault conditions.  
In addition, the test group error results for asymmetrical fault conditions further show that the identification method and algorithm proposed in this paper can more effectively characterize the transient operation characteristics of actual WTGs under asymmetrical high- and low-voltage ride-through faults and show stronger applicability to the actual working conditions. Meanwhile, the robustness of the GQNA algorithm under complex fault conditions is also verified again, which provides a more reliable theoretical basis and technical support for the analysis of the operational characteristics of WTGs under asymmetrical faults.