## 5 Conclusions  
This paper presents a study of the performance of a Solar PV system, using a controller with the traditional P&O and the proposed fuzzy logic enhanced P&O which is a P&O improved with fuzzy logic algorithm. The fuzzy logic increases the performance of the P&O algorithm, enabling the system to adapt faster to variable environmental conditions, leads to a MPP faster and an equal performance in steady state conditions when MPP is reached, leading to higher generated energy. This is mainly due to the duty cycle in the fuzzy logic enhanced P&O not having a fixed value, allowing the converter to have a smoother and faster adjustment to changing environment conditions and steady state conditions, fixing one of the problems of the traditional MPPT algorithms regarding the trade off between performance at steady state and changing environment conditions. The proposed traditional P&O achieves a similar performance in steady state conditions, due to its low fixed step size, but in dynamic conditions it takes a longer time to reach MPP and can guess it incorrectly, problems that were solved by the proposed FLC. This results in lower energy losses until the MPP is reached and consequently, higher generated energy. The peak results obtained for the current and voltage, never reached the maximum of 11 A and 1000 V, respectively, defined by the dimensioned components and the \(r\) coefficient values which is between 0.3 and 0.6, as recommended by the state of the art. It was concluded that a big increase in the solar irradiation value, creates the biggest peaks in the measured values and the system has a higher difficulty to reach the MPP. The increases in the ambient temperate, lowers the voltage output and input of the converter, while the solar irradiation increases, increases the input current, as such the biggest value of the generated power was obtained in the last interval, where the solar irradiation had the highest and the temperature the lowest simulated value, out of the analyzed conditions. The sudden increase in solar irradiation is than the most critical environment condition, affecting the performance of the MPPT algorithms since every time there was only an increase of this parameter, the traditional P&O either had a slow response (first two seconds), or had an incorrect guess of the MPP value (second interval) and thus clearly \begin{table}
\begin{tabular}{c c c} \hline \hline
**MPPT Algorithm** & **P\&O** & **Proposed FLP\&O** \\ \hline Response time in changing & & & \\ environment conditions & slow & fast \\ \hline Correct decision in changing & & & \\ environment conditions & difficult to reach & fast to reach \\ \hline Stability & & stable \\  & (due to low fixed step size) & stable \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison between the traditional P&O and the proposed FLP&O. showing how the proposed fuzzy logic enhanced P&O, can improve the performance of the solar PV system by effectively solving these problems. Both algorithms achieved a converter efficiency around 98% for the boost converter, the same as the value given by the manufacturer for the complete inverter, which means the value should be higher. While since it is close to 100%, any improvement would be practically negligible so the model can be considered valid for the study of the PV system. As a future study, the creation of a test prototype is recommended, to verify if adding variables such as ease of implementation and cost of the prototype, mean fuzzy logic enhanced P&O should be used, or just the traditional P&O. It is expected that the fuzzy logic enhanced P&O requires more programming knowledge and computational power, although it isn't expected that the circuit board will be significantly changed, since both algorithms only directly control the voltage and power at the output of the PV panel.