## 4 Results and Discussion
The environment conditions of this specific interval, are the same as the test conditions for the PV panels, with a 1000 W/m\({}^{2}\) solar irradiation and an ambient temperature of 25 \({}^{\circ}\)C, so the generated power, the input current and the voltage of the boost converter should be nearly equal to the MPP values presented in Table 1 of Section 2.1, which is corroborated by Figure 15.  
It is very hard to directly compare the results obtained for the proposed algorithms, to other works since each work has a converter with different parameters and studies the performance of the PV systems in different sets of ambient temperature and solar irradiation values. During this work it was noted that a capacitor with high value and an inductor with low value, positively impact the efficiency of the converter, so it is concluded that the best way to validate our results is to directly compare to the real model the converter is based on. In both algorithms, the efficiency is always nearly the same except at the beginning when the values didn't stabilize, for the traditional P&O the value is 97.71% and for the fuzzy logic enhanced variant the value is 97.82%, when in steady state both reach 97.90%. In both converters the value is almost equal to the 98% value, given by the manufacturer for the whole inverter [25]. While the inverter also has a DC-AC converter after the boost converters as it can be seen in Figure 4 of Section 2.1, which means its efficiency must be slightly higher, but since it is close to 100%, any improvement in the model to reach a higher efficiency would be almost negligible and wouldn't have a big impact in the obtained results. However, since the model performs almost equally according to the information given by the manufacturer, the model can be considered valid to study the parameters of a PV system, which adds further credibility regarding the results obtained for both controllers performances. In addition to this, the peak voltage and current values of the components (1000 V and 11 A, respectively), were never reached so the components of the converter can safely sustain the energy generated by the system. The slightly higher efficiency obtained for the fuzzy logic algorithm, compared to the traditional P&O, is due to faster adaptability to changing environment conditions and better stability in steady state conditions. It is important to note, that the efficiency value doesn't always translate to higher generated energy, as it can be seen in Figures 15 and 16, in the interval between 2 and 4 s, the P&O controller reached the wrong MPP at first, while the fuzzy logic variant guessed it correctly immediately. Consequently the generated energy was noticeably higher in the system using the fuzzy logic enhanced P&O controller, despite the efficiency being nearly the same in this interval. The proposed fuzzy logic enhanced P&O controller has improved the performance of the solar PV system both in steady state and in dynamic environment conditions, over the traditional P&O MPPT controller as summarized in Table 5. These improvements lead to a higher generated energy, improving the system's overall efficiency. One of the biggest contributions to these results, is the fact that in the fuzzy logic enhanced P&O, the step size is not fixed unlike the traditional P&O, which enables the controller to more properly adjust the system to changing environment conditions (especially the solar irradiation change), reaches the faster correct MPP and also in improving the performance in steady state conditions, by minimizing the oscillations when MPP is achieved. This controller can thus reaches improvements in both dynamic and steady state conditions, contrary to traditional controllers using a fixed step size, in which it has to be done a trade-off between the performance in both conditions.