## 5 Simulation Example  
To validate the effectiveness of the proposed load frequency control scheme based on switched systems, this study employs a three-area interconnected power system as the simulation model. The system parameters are derived from data in reference [15] and are provided in Table 2. The total sampling time is denoted by 60 s, with a sampling period of \(\text{T}=0.001\) s. Other parameters include \(\text{T}_{12}=0.21\), \(\text{T}_{13}=0.24\), \(\text{T}_{23}=0.13\). Assuming a data transmission success rate of \(\mathbb{E}\{\alpha(k)\}=0.6\), the controller parameters are denoted by \(\eta_{1}=0.3,\eta_{2}=0.5,\mu_{1}=1.4,\mu_{2}=1.2,\rho_{1}=2.5,\rho_{2}=2.8, \lambda_{1}=1.2,\lambda_{2}=2.7\), \(\Omega=10^{16}\), \(\delta=0.003\) and the initial responses for the three regions are represented by \(u_{j}(1)=0,\hat{\phi}_{j}(1)=0.3\). Furthermore, a load disturbance of 0.02 per unit (p.u.) is introduced simultaneously in each region. The system frequency deviation curves, tie-line power variation curves, and system output curves are depicted in Figures 2-4. Notably, after running for a certain duration, the deviations in frequency and variations in tie-line power within each region eventually diminish to zero. The simulation results demonstrate the favorable control performance of the proposed algorithm. Figure 5 shows the switching signals. \begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Area 1** & **Area 2** & **Area 3** \\ \hline \(D/(\text{pu}/\text{Hz})\) & 1.0 & 1.5 & 1.8 \\ \(M/(\text{pu}\cdot\text{s})\) & 10 & 12 & 12 \\ \(R/(\text{Hz}/\text{pu})\) & 0.05 & 0.05 & 0.05 \\ \(T_{t}/\text{s}\) & 0.30 & 0.17 & 0.20 \\ \(T_{g}/\text{s}\) & 0.37 & 4 & 0.35 \\ \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters for the power system. Figure 4: The curve of \(ACE_{j}\). Figure 3: The response curve of change in tie-line power. Figure 2: The curve of frequency deviation response. The release times and intervals between sensor outputs are depicted in Figures 6-8, with 255,669, 36,389 and 22,658 trigger occurrences among all sample intervals. Additionally, a comparison was made with the PI control method and frequency curves under the PI and SSMFAC control methods have been plotted in Figure 9. Based on the graph analysis, the SSMFAC control strategy demonstrates superior performance compared to the PI control scheme, showcasing smaller overshoot and faster convergence speed. This observation indicates the effectiveness of the SSMFAC approach in achieving better control system performance. Additionally, we utilized the \"tic\" and \"toc\" functions in MATLAB to measure the average running time of the two algorithms, which were 1.734 s and 2.2505 s, respectively. The results indicate that the SSMFAC method offers a low computational burden while ensuring control performance. In Table 3, we evaluated the effectiveness of the proposed approach using two performance criteria: the Integral of Absolute Error (IAE) and the Integral of Time multiplied by Absolute Error (ITAE). We compared the results with  
Figure 5: Any 100 switching sequences in the system. Figure 6: Event triggering intervals in area 1. While the event-triggered SSFMAC algorithms (20) and (22) have demonstrated effectiveness in theoretical simulations, it is important to consider practical factors that can influence system control. One such factor is the susceptibility to noise; we have incorporated noise into our simulations to evaluate the robustness of the SSFMAC algorithm under realistic conditions. We modeled noise with mean 0 and variance 0.00000003 as Gaussian white noise added to the input signal. Figure 10 shows that the system remains stable and performs satisfactorily, but the control accuracy is slightly degraded due to noise. Including noise in the simulations highlights the practical challenges the SSFMAC algorithm faces. While the algorithm remains robust under noisy conditions, additional filtering techniques are necessary to maintain control performance.