## 5 Case Study and Analysis
### Analysis and Comparison of Results  
Using the number of hidden layers and neurons determined in Section 5.2, the trained DNN is obtained through deep learning on the training set. Table 1 provides a comparison between the time and MSE required for online decision-making on the test samples using the proposed method and time-domain simulation. To acquire the optimal combined inertial control combination under 4200 conditions, the time-domain simulation would require at least 4200 simulation calculations, each taking about 120 s. This would result in a total simulation time of approximately 140 h. In contrast, using the method presented to regulate the frequency of 4200 conditions results in an MSE of 0.00117 and an entire time of only 0.31 s, significantly reducing the time required \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Offline Training** & **Online Decision** & **MSE of Training** & **MSE of** \\  & **Time (s)** & **Time (s)** & **Set** & **Validation Set** \\ \hline PSO & \(\backslash\) & 504,000 & \(\backslash\) & \(\backslash\) \\
1 layer DNN & 60 & 0.23 & 0.00161 & 0.00169 \\
2 layers DNN & 75 & 0.26 & 0.00125 & 0.00142 \\
3 layers DNN & 82 & 0.31 & 0.00112 & 0.00117 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the effects between time-domain simulation method and different DNN. and improving efficiency. Figure 9 displays the predicted results of the optimal combined inertial control parameter combination. As depicted in Figure 9, as the load disturbance increases, while keeping wind speed and wind power proportion constant, the optimal droop control parameter \(K_{\text{pf}}\) is gradually increasing and the optimal virtual inertia control parameter \(K_{\text{df}}\) is gradually decreasing. With fixed wind speed and load disturbance in the scene, both \(K_{\text{pf}}\) and \(K_{\text{df}}\) are decreasing as the wind power proportion increases. With the increase in wind speed, when the proportion of wind power and load disturbance in the scene are fixed, both \(K_{\text{pf}}\) and \(K_{\text{df}}\) are decreasing relatively slowly. In summary, it can be seen that a higher amount of load disturbance leads to a larger power deficit in the system, making the minimum frequency value of the system smaller. At this time, more \(K_{\text{pf}}\) is needed to reduce the maximum frequency deviation. However, if it is too large, it will lead to a more serious secondary drop in frequency. For this reason, there will be a corresponding reduction in \(K_{\text{df}}\). The higher the wind power proportion, the lower the inertia in the system, and the greater the unbalanced power in the system when the WT exits the frequency regulation. If the conventional thermal power generator cannot provide enough frequency regulation power, the frequency secondary drop is more serious. Reducing the combined inertial control parameters can reduce the rotor kinetic energy released when the WT is involved in frequency regulation, thus mitigating the frequency secondary drop. The increase in wind speed will lead to a higher real-time power generation by the WT, but during the short-term overshoot phase, using the same combination of parameters for combined inertial control will result in a higher energy consumption and a drop in the secondary droop point. The optimal combined inertial control parameters should decrease slowly in order to achieve improved frequency regulation. Furthermore, the prediction models exhibit high accuracy, exceptional generalization capability, and strong adaptability across different scenes. Online decision-making is carried out using the methods described above in a system scenario where the wind speed is 6 m/s, the wind power proportion is 30%, and the load disturbance is 0.05 p.u., starting at 40 s. In this scenario, the performance of combined inertial control with BP training parameters is compared with frequency control without wind power participation. The results of frequency control are presented in Figure 10a.