## 4 Results and Discussion  
A dynamic simulation of a solar PV system using the traditional P&O controller and a PV system using the fuzzy logic enhanced P&O (FLP&O) controller, has been carried out to analyze the parameters: input voltage, current, power, efficiency and output voltage of the converter. The first three parameters were chosen to verify how close to the MPP values each algorithm is. The efficiency parameter was chosen to conclude, if an algorithm achieves a significantly higher efficiency and the output voltage was chosen to verify, visible oscillations exist at the output converter. The obtained results are presented in Figures 15-17. Figure 14: Overview of the control rules. Figure 15: (a) Voltage, (b) Current and (c) Power waveforms of the Solar PV panels for Perturb and Observe (P&O) and fuzzy logic enhanced P&O (FLP&O) algorithms. As can be seen in the figures above, the P&O algorithm needs more time to stabilize at the beginning of the simulation. In the interval that starts at 2 s, this becomes clearer, as the FLP&O stabilizes in a point where the generated power is higher than the point where the P&O initially stabilizes, this means that this controller reached the correct MPP faster than the traditional algorithm. When both attain the MPP, the oscillations are the same or slightly lower for FLP&O as it can be seen mainly in Figure 16, due to the efficiency value being more stable. A higher fixed step size should have been used, to more clearly show the improvements that FLP&O can present in reducing oscillations at MPP in the steady state conditions. These results show that the adaptive step size makes the proposed fuzzy logic enhanced P&O controller more stable at steady state conditions, faster to adapt to changing environment conditions and consequently more efficient, since less losses occur when trying to reach a new MPP, compared to the traditional P&O. From the simulation results, it was possible to notice that the biggest peaks were obtained when only the solar irradiation was increased, which lead to greater difficulties in reaching the MPP when the environmental conditions reach the steady state. In the interval between 4 and 6 s, both solar irradiation and the ambient temperature changed and the traditional P&O algorithm had an easier time to adapt and achieve the correct MPP, compared to the previous intervals. Therefore, changing only the solar irradiation leads to a higher difficulty for the P&O controller to adapt and a more noticeable lower  
Figure 16: Efficiency of the boost converter for P&O and FLP&O algorithms. Figure 17: Output voltage waveform of the converter for P&O and FLP&O algorithms. performance, when compared to the proposed fuzzy logic enhanced P&O controller, in addition to this, the fuzzy logic enhanced P&O controller found correctly the MPP almost immediately, leading to higher generated energy. It was also noted that the current and the output voltage, always rise when the irradiation increases, and when the temperature increases, the value of the input and output voltage is lowered. The current value is unaffected by the temperature variation, leading to lower generated power since the voltage decreases when the temperature increases, which is justified by the lower convection losses due to higher temperatures, which negatively impact the PV system performance. In accordance to the observed patterns, as expected, the last interval that begins at second 8, which has the lowest simulated temperature and highest irradiation, produced the maximum generated power.