# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 7 ANN Results with Simulated Degradation on _I-V_ Curves
### Improving the ANN Results with Repeated Tests  
By assuming that a degradation phenomenon is occurring permanently, the ANN parameters identification method can be executed frequently (e.g., more than one time per day) without affecting the normal operation of the PV system, and the \(\mathbf{\Delta p_{\%}}\) can be estimated for all cases. Since the effective degradation of the PV panel is not related to the changes in the environmental conditions, the average values of \(\mathbf{\Delta p_{\%}}\) are considered for all tests collected in a short period (e.g., one day). Table 10 shows the average percentage variation of the SDM parameters estimated with the ANN when the process described in the previous section is repeated for a number of trials \(N_{test}=100\) selected randomly among different environmental conditions. Each row in the table refers to the parameters variation reported in the first column. For example, for the first row, a \(-4\%\) of induced degradation on \(I_{ph}\) is estimated, on average, with \(-3.73\%\).  
It worth noting that some residual cross-coupled variations appear in the estimation of the other parameters. Nevertheless, if we take into account the different sensitivities of the \(I\)-\(V\) curve with respect to each parameter, these crossed variations can be acceptable. Indeed by considering the sensitivity values reported in (4) and by referring to the first row of Table 10, the \(-3.73\%\) reduction in \(I_{ph}\) is reflected in the reduction of 3.76% in the delivered power, while a \(-22.3\%\) of variation in \(R_{sh}\) corresponds to 0.69% power reduction.  
Another small anomaly is in the second row where the identification of the variation of the saturation current is not detected accurately (+103%) with respect to the induced  
Figure 17: ANN identification of degraded curve with \(-80\%\) of variation on \(R_{sh}\) for two different irradiance and temperature conditions.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Applied Degradation: \(R_{sh}^{deg}=0.2R_{sh}\) (\(-80\%\))**} \\ \hline \(G\) [\(\frac{\mathbf{W}}{\mathbf{m}^{2}}\)] & \(T\) [\({}^{\circ}\)C] & & \(I_{ph}\)[A] & \(I_{sat}\) [\(\mu\)A] & \(\eta\) [-] & \(R_{s}\) [\(\Omega\)] & \(R_{sh}\) [\(\Omega\)] \\ \hline \multirow{4}{*}{835} & \multirow{4}{*}{44} & \(\mathbf{P_{H}}\) & \(4.165\) & \(0.615\) & \(1.297\) & \(0.240\) & \(454\) \\  & & \(\mathbf{P_{deg}}\) & \(4.170\) & \(0.614\) & \(1.304\) & \(0.247\) & \(107\) \\  & & \(\Delta\mathbf{p_{\%}}\) & \(0.12\) & \(-0.1\) & \(0.55\) & \(2.7\) & \(-76.4\) \\ \cline{3-8}  & & \(\mathbf{\Delta P_{mpp,\%}}\) 0.9 & \(0.12\) & \(0.013\) & \(0.57\) & \(-0.16\) & \(-3.04\) \\ \hline \multirow{4}{*}{1000} & \multirow{4}{*}{53} & \(\mathbf{P_{H}}\) & \(5.023\) & \(1.670\) & \(1.301\) & \(0.246\) & \(493\) \\  & & \(\mathbf{P_{deg}}\) & \(5.051\) & \(1.791\) & \(1.298\) & \(0.243\) & \(191\) \\ \cline{1-1}  & & \(\Delta\mathbf{p_{\%}}\) & \(0.56\) & \(7.3\) & \(-0.18\) & \(-1.1\) & \(-61.2\) \\ \cline{1-1} \cline{2-8}  & & \(\mathbf{\Delta P_{mpp,\%}}\) 0.9 & \(0.57\) & \(-0.6\) & \(-0.24\) & \(0.09\) & \(-1.06\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: SDM parameters estimated with ANN for \(I\)-\(V\) curves of Figure 17.  
degradation of +150% on \(I_{sat}\). In this case, part of the induced degradation on \(I_{sat}\) is translated in a variation of \(\eta\). This can be easily justified by the fact that variations on \(I_{sat}\) and on \(\eta\) parameters produce the same deformation on the \(I\)-\(V\) curve (see S2 and S3 effects in Figure 2); thus, for the ANN, it is more difficult to detect the origin of degradation when they produce similar deformations on the \(I\)-\(V\) curve.  
Finally, the last row indicates the average errors on the estimated SDM parameters when no degradation is applied. Here, it is evident that in the presence of healthy curves, the estimation of the SDM parameters variation tends toward small values, confirming that no degradation is occurring.