# Centralized MPPT Control Architecture for Photovoltaic Systems Using LoRa Technology
## 6 Discussion  
In order to extrapolate the simulation results to those that would have been obtained in the real system, it was necessary to validate the simulation model. To that purpose, we used the irradiance and temperature measurements from the experimental results of sudden changes in irradiance that are shown in Figures 22 and 23. The simulation results are presented in Figures 24 and 25. When comparing Figure 22 to Figure 24 (P&O algorithm), as well as Figure 23 to Figure 25 (SMC algorithm), it can be observed that the simulation model presented very similar results in the power and duty cycle during the experiments, which validated the implemented simulation model. In addition, the experimental results shown in Section 5 demonstrate the viability of the LoRa wireless controller schema in both constant and smoothly changing conditions. In such cases, the controller's time step proved sufficient to adapt to changes and to achieve maximum power extraction. While the controller could operate more swiftly by adjusting the change in duty cycle, this action may result in increased oscillations around the MPP, potentially leading to a loss in efficiency. A larger variation in control actions means a more substantial change in the operating point, which allows the algorithm to explore  
Figure 23: The experiment results of the sudden irradiance change conditions for the SMC controller. (**a**) Cell temperature. (**b**) Solar irradiance. (**c**) PV power. (**d**) Measured PV voltage and reference PV voltage. (**e**) Duty cycle. the power-voltage curve more quickly. This increased exploration speed can make the controller respond faster to changes in environmental conditions. Figure 24: Simulation of the P&O controller under abrupt irradiance variations. (**a**) Cell temperature. (**b**) Solar irradiance. (**c**) PV power. (**d**) Duty cycle. Figure 25: Simulation of the SMC controller under abrupt irradiance variations. (**a**) Cell temperature. (**b**) Solar irradiance. (**c**) PV power. (**d**) Measured PV voltage and reference PV voltage. (**e**) Duty cycle. LoRa's constraints on data size did not pose an issue as the application's capacity requirements in this regard were not demanding. The selected LoRa parameters resulted in a round trip latency of 164 ms approximately, but the execution time in the computing node and the sensor and actuator nodes must also be considered. During the experiments, a count of lost packages was kept. We observed that a control time step of 300 ms was the minimum latency required for a reliable communication. In actuality, no packet loss was found, but we observed packet losses with shorter time steps. However, we considered a control time step of one second to have a margin of safety. The same tests showed that the probability of packet loss with a one second time step was reduced to practically zero. These results demonstrate the robustness of LoRa communications for this application with a properly chosen time step. However, it is worth noting that the performance of this control scheme was suboptimal when confronted with significant changes in irradiance. In such instances, QoS limitations associated with sampling time amplify the drift phenomenon inherent in P&O controllers. While not critical, this issue results in operating at a less efficient point until conditions stabilize and the controller can return to the MPP. Increasing the controller's duty cycle change in each action, as discussed earlier, would alleviate this problem, but undesirable consequences deter such an adjustment. Ultimately, an increased control action change makes the controller quicker, reducing the time the controller is in inefficient conditions and, therefore, reducing the power loss in the transient. Nonetheless, this increased control action also affects the steady state, making the oscillations bigger and, thus, losing efficiency. Finding the right balance becomes a must.