## 3 Results and Discussion
### Experimental Results  
TEG systems are extremely risky and challenging to work with in real-time applications. Using an equivalent model to the TEG emulator described in Section 2, an emulator circuit is designed. TEG is a temperature-dependent voltage source whose open-circuit voltage (Voc) varies with the temperature difference. To simulate the internal resistance (\(R_{TEG}\)) of a TEG module, a power source with a high wattage and low resistance is connected in series. To simulate the temperature changes, a rapid-voltage-changing DC source is altered by shorting the diode array. Figure 14 depicts the connections between the electrical components. Table 4 lists the parts that were utilized to establish the MPPT control for the TEG system. In addition to the limitations, the sensor noise sensitivity in the measurement  
Figure 13: Graphical representation of MPPT rating. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Tech. Name** & \begin{tabular}{c} **Tuning** \\ **Parameter** \\ **Number** \\ \end{tabular} & **Rand. No.** & \begin{tabular}{c} **Termination** \\ **Criteria** \\ **Achieved?** \\ \end{tabular} & **Avg. Effc.** & \begin{tabular}{c} **Avg. Effc.** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Hardware** \\ **Modification** \\ \end{tabular} & \
\begin{tabular}{c} **Response** \\ **Time in** \\ **Variation (s)** \\ \end{tabular} &
\begin{tabular}{c} **Rating** \\ **Score** \\ \end{tabular} \\ \hline MRA & 1 (1) & 2 (2) & No (1) & 0.23 (1) & 99.96 (1) & No (1) & Fast (2) & 1.285 \\ \hline WOA & 3 (3) & 1 (1) & Yes (2) & 0.35 (1) & 99.86 (1) & Yes (2) & Slow (3) & 1.857 \\ \hline GWO & 2 (2) & 2 (3) & Yes (2) & 0.49 (1) & 99.65 (1) & No (1) & Very slow (4) & 2.000 \\ \hline PSO & 3 (3) & 2 (3) & Yes (2) & 0.60 (2) & 99.42 (2) & No (1) & Very slow (4) & 2.423 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: MPPT rating comparison. also affects the MPPT control performance by causing fluctuations in the control system. To overcome these limitations and achieve satisfactory performance in MPPT control, careful design and implementation of the Mud Ring Optimization (MRO) algorithm is crucial. This study contributes to the advancement of the MPPT control for TEG systems and the implementation of renewable energy technologies. The MRO algorithm demonstrates exceptional performance in dynamic thermal environments, tracking the maximum power point with a higher efficiency than conventional MPPT methods. The experimental results again show an average power output of 99.95% and faster response time of 220 ms, thus resulting in the faster tracking of the global maximum power point (GM) under dynamic conditions, with an improvement of up to 70% less tracking time. MRA has a powerful reaction with effective completion well within 200 ms, which contrasts GWO and PSO. Lower time, less oscillations, and excellent efficiency are all achieved using the MRA. Figure 15 demonstrates the power tracked by the MRA. The \begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Magnitude** \\ \hline Current at MPP (I\({}_{\text{max}}\)) & 1.0702 A \\ \hline Ideality factor & 1.0037 \\ \hline Maximum power (P\({}_{\text{max}}\)) & 20.1 W \\ \hline Peak efficiency & 19.3\% \\ \hline Open circuit voltage (V\({}_{\text{oc}}\)) & 22.7 V \\ \hline Series resistance \(R_{s}\) & 1.0547 \\ \hline Short circuit current (I\({}_{\text{SC}}\)) & 1.17 A \\ \hline Shunt resistance \(R_{sh}\) & 405.96 \\ \hline Temperature coefficient of I\({}_{\text{sc}}\) & 0.043\%/K \\ \hline Temperature coefficient of V\({}_{\text{oc}}\) & \(-\)0.35\%/K \\ \hline Voltage at MPP (V\({}_{\text{max}}\)) & 18.76 V \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The electrical characteristics of hardware modules. Figure 14: Hardware setup figure. GWO has a different foraging strategy and exhibits a marginal improvement in settling time, requiring 350.