## 5 Simulation Example Analysis
### Verify Asymmetric Fault Identification Results  
The actual wind turbine operation needs to consider not only symmetrical faults but also the occurrence of asymmetrical faults. In order to further verify the correctness of the model, the proposed parameter identification method, and the adaptability to the working conditions of different test groups, the working conditions of voltage drop to 0%, 20%, 35%, 50%, and 75% under the conditions of asymmetric medium power and small power are used as the test groups of asymmetric LVRT fault ride-through for the actual test identification, and the working conditions of voltage drop to 50% under the conditions of asymmetric high power are used as the verification group of the identification results of asymmetric LVRT fault ride-through. Similarly, the working conditions of voltage rise to 120%, 125%, and 130% under asymmetrical medium power and low power conditions were used as the test groups for asymmetrical high voltage ride-through fault identification, and the working conditions of voltage rise to 125% under asymmetrical high power conditions were used as the validation groups for asymmetrical high voltage ride-through identification results.  
The results of control parameter identification of different algorithms under asymmetric low-voltage ride-through and high-voltage ride-through test conditions are shown in Tables 10 and 11, respectively; the comparison between simulation data and measured data of different algorithms under asymmetric low-voltage ride-through test conditions is shown in Figure 20a, and the comparison between simulation data and measured data of different algorithms under asymmetric high-voltage ride-through test conditions is shown in Figure 20b. The deviations of the different algorithms' identification simulation results under asymmetrical LVRT and HVRT test conditions are compared in Figures 21 and 22; the steady-state average deviations of the different algorithms' control identification simulation results during fault ride-through under asymmetrical LVRT and HVRT test conditions are shown in Tables 12 and 13, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **NEAM** & **PSO** & **SA** & **GQNA** \\ \hline K\({}_{\text{VP}}\) & 0.441 & 0.7847 & 0.74 & 0.424 \\ K\({}_{\text{id}}\) & 0.0852 & 0.0917 & 0.1135 & 0.0895 \\ I\({}_{\text{deset}}\) & 0.3844 & 0.152 & 0.0996 & 0.3444 \\ K\({}_{\text{vq}}\) & 1.962 & 1.9842 & 1.7895 & 1.902 \\ K\({}_{\text{iq  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **NEAM** & **PSO** & **SA** & **GQNA** \\ \hline K\({}_{\text{vp}}\) & \(-0.7404\) & \(-0.0766\) & \(0.0269\) & \(-0.7438\) \\ K\({}_{\text{id}}\) & \(0.8901\) & \(0.888\) & \(0.8926\) & \(0.8889\) \\ I\({}_{\text{dset}}\) & \(0.8529\) & \(0.0764\) & \(-0.0309\) & \(0.8254\) \\ K\({}_{\text{vq}}\) & \(4.8718\) & \(3.5934\) & \(0.2367\) & \(4.8718\) \\ K\({}_{\text{iq}}\) & \(3.9141\) & \(3.6028\) & \(-8.383\) & \(3.914\) \\ I\({}_{\text{qset}}\) & \(0.1513\) & \(0.1453\) & \(-0.0574\) & \(0.1913\) \\ K\({}_{\text{iq\_start}}\) & \(16.0941\) & \(16.093\) & \(16.0628\) & \(15.9927\) \\ I\({}_{\text{qset\_start}}\) & \(0.045\) & \(0.045\) & \(0.0449\) & \(0.0447\) \\ \hline \end{tabular}
\end{table}
Table 11: Recognition results of different algorithms for asymmetrical HVRT conditions.  
Figure 20: _Cont._Figure 21: Comparison of the deviation calculation results of different algorithms for high-power asymmetric LVRT conditions.  
Figure 20: Comparison of the high-power asymmetrical LVRT and HVRT simulation and measured data: (**a**) symmetrical LVRT voltage drops to 50%; (**b**) symmetrical HVRT voltage rises to 125%.