# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
## 4 Simulation Results  
To validate the proposed method, simulation studies were conducted using MATLAB/Simulink (R2022b) on a standard desktop computer. The microgrid test system, with parameters detailed in Table 1, was modeled in the Simulink environment based on the architecture described in [17]. The microgrid includes four DREGUs, as shown in the schematic diagram in Figure 3, and the model includes bidirectional communication links, which more closely mirror the operation of actual power systems. This dynamic model, encompassing DREGUs, power lines, and loads, and serves as the data generation source;  
Figure 2: Data-driven control framework for AC microgrids.  
it provides the necessary input-output data (i.e., measured output voltages and control inputs) to the controller at each simulation time step. The control parameters listed in Table 2 were determined through a systematic evaluation of system performance and a parameter sensitivity analysis, rather than a simple trial-and-error process. To ensure a practical and rigorous simulation, the key parameters were carefully selected. We set the sensor saturation threshold to 315 V, a value slightly above the 311 V rated voltage, to realistically test the algorithm's robustness against nonlinear clipping constraints during transients. Concurrently, we set the number of iterations to 30. This duration is sufficient to fully demonstrate the entire convergence process and steady-state performance, as our preliminary tests showed the tracking error consistently converged within 15-20 iterations, thus avoiding unnecessary computational overhead. The primary objective was to achieve a rapid convergence speed and high tracking accuracy while ensuring system stability and avoiding excessive overshoot. For instance, the step-size parameter \(\beta_{1}\) was adjusted to balance the response speed against oscillations, while the penalty factor \(\delta_{1}\) was set to a small positive value to ensure the denominator in the update law remains robustly non-zero. For comparison, the conventional PI controller used as a baseline was also carefully tuned using the Ziegler-Nichols method followed by manual fine-tuning to optimize its performance under the same test conditions, thereby ensuring a fair and meaningful comparison. Through three specific case studies, this paper verifies the effectiveness of the proposed strategy.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Symbol** & **Parameter** & **Value** \\ \hline \(y_{0}\) & Rated line voltage & 311 V \\ \(Z_{line,1}\) & Line impedance of DG1 & 0.002 + j0.02 \\ \(Z_{line,2}\) & Line impedance of DG2 & 0.004 + j0.03 \\ \(Z_{line,3}\) & Line impedance of DG3 & 0.002 + j0.02 \\ \(Z_{line,4}\) & Line impedance of DG4 & 0.004 + j0.03 \\ \(S_{rate,1}\) & Rated power of DG1 & 20 kW, 10 kVar \\ \(S_{rate,2}\) & Rated power of DG2 & 20 kW, 15 kVar \\ \(S_{rate,3}\) & Rated power of DG3 & 20 kW, 20 kVar \\ \(S_{rate,A}\) & Rated power of DG4 & 20 kW, 25 kVar \\ \(L_{f}\) & DG filter inductance & 1 mH \\ \(C_{f}\) & DG filter capacitor & 100 μF \\ \(load1\) & Capacity of load1 & 12 kW, 12 kVar \\ \(load2\) & Capacity of load2 & 15.3 kW, 7.6 kVar \\ \(load3\) & Capacity of load3 & 10 kW, 10 kVar \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters.  
Figure 3: Diagram of 4-DREGU islanded microgrid test system.