# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 4 Results
### GA Accuracy  
After specifying the crossover and mutation probability and setting the decimal precision to two, some experiments have been performed to measure the accuracy of the  
Figure 12: Crossover and mutation tests with crossover fixed to \(p_{c}=0.8\). It is worth mentioning that, in Table 6, for a low irradiance value (214.72), it can be easily observed that, as temperature increases, the accuracy of the algorithms decreases. In particular, when the temperature equates 50 \({}^{\circ}\)C, the real value diverges from the predicted one by 18%. That noticeable deviation between the real and predicted values is not attributed to the genetic algorithm but to the PV modeling. PV parameters depend on temperature and irradiance but are usually not taken into account in the equations to simplify the modeling process, leading to less accurate solutions when irradiance or temperature diverge a lot from the standard conditions. Various PV modeling methods are proposed in [25]. On the contrary, in Table 7, when the irradiance value (500 W/m\({}^{2}\)) is closer to that of the standard conditions, better results were obtained, while the precision in every quantity \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{5}{c}{**Irradiance 1000 W/m\({}^{2}\)**} \\ \hline  & \multicolumn{4}{c}{**Temperature 0 \({}^{\circ}\)C**} \\ \hline  & **Real Values** & **Genetic** & **Error** & **Accuracy \%** \\ \hline \(I_{mpp}\)**A** & 4.800 & 4.74547 & 0.05453 & 99 \\ \hline \(V_{mpp}\)**V** & 40.110 & 39.28088 & 0.82912 & 98 \\ \hline \(P_{mpp}\)**W** & 192.550 & 186.40623 & 6.14376 & 97 \\ \hline \multicolumn{5}{c}{**Temperature 25 \({}^{\circ}\)C**} \\ \hline \(I_{mpp}\)**A** & 4.830 & 4.77966 & 0.05034 & 99 \\ \hline \(V_{mpp}\)**V** & 36.220 & 36.63148 & \(-0.41148\) & 99 \\ \hline \(P_{mpp}\)**W** & 174.984 & 175.08602 & \(-0.10202\) & 100 \\ \hline \multicolumn{5}{c}{**Temperature 50 \({}^{\circ}\)C**} \\ \hline \(I_{mpp}\)**A** & 4.790 & 4.82245 & \(-0.03245\) & 99 \\ \hline \(V_{mpp}\)**V** & 32.530 & 33.81646 & \(-1.28646\) & 96 \\ \hline \(P_{mpp}\)**W** & 155.820 & 163.07818 & \(-7.25818\) & 95 \\ \hline \end{tabular}
\end{table} 
Table 8: GA accuracy tests with irradiance 1000 W/m\({}^{2}\). \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{5}{c}{**Test Results**} \\ \hline  & \multicolumn{6}{c}{**Irradiance 1000 W/m\({}^{2}\)**} \\ \hline  & \multicolumn{6}{c}{**Temperature 0 \({}^{\circ}\)C**} \\ \hline  & \multicolumn{6}{c}{**Temperature 0 \({}^{\circ}\)C**} \\ \hline  & \multicolumn{6}{c}{**Real Values**} & **GA** & **SLSQP** & **Cobyla** & **Rao-1** & **Rao-2** & **Rao-3** \\ \hline
**Impp** & 4.80 & 4.75 & 4.72 & 3.82 & 4.74 & 4.73 & 4.73 \\ \hline
**Vmpp** & 40.11 & 39.28 & 39.49 & 43.65 & 39.45 & 39.48 & 39.48 \\ \hline
**Pmpp** & 192.55 & 186.41 & 186.48 & 166.79 & 187.20 & 186.50 & 186.50 \\ \hline  & \multicolumn{6}{c}{**Temperature 25 \({}^{\circ}\)C**} \\ \hline
**Impp** & 4.83 & 4.78 & 4.78 & 3.30 & 4.80 & 4.79 & 4.79 \\ \hline
**Vmpp** & 36.22 & 36.63 & 36.63 & 42.12 & 36.59 & 36.62 & 36.62 \\ \hline
**Pmpp** & 174.98 & 175.09 & 175.10 & 138.78 & 175.02 & 175.08 & 175.08 \\ \hline  & \multicolumn{6}{c}{**Temperature 50 \({}^{\circ}\)C**} \\ \hline
**Impp** & 4.79 & 4.82 & 4.82 & 2.87 & 4.81 & 4.82 & 4.82 \\ \hline
**Vmpp** & 32.53 & 33.82 & 33.82 & 40.68 & 33.78 & 33.81 & 33.81 \\ \hline
**Pmpp** & 155.82 & 163.08 & 163.10 & 116.88 & 162.95 & 163.09 & 163.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Output results of SLSQP, COBYLA and Rao (1,2,3) algorithms. of electrics does not fall below 96%. Similar results can be illustrated in Table 7, where accuracy remains above 95%. Due to the fact that electric quantities (voltage and current) can be predicted with an error less than 10%, except for cases of extreme values of irradiance and temperature, an error could be raised when real and predicted values diverge more than 10%.