# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 8 ANN Results with Experimental Degraded \(I\)-\(V\) Curves  
The developed method is also tested with experimental \(I\)-\(V\) curves, where the series resistance degradation is applied by connecting in series to the PV module a small resistance of value (\(\Delta R_{s}\)).  
The experimental data refer to a _Isofton I-53_ PV module installed on the roof of the _Department of Applied Physics II_ at the University of Malaga (latitude: 36.715\({}^{\circ}\) N ; longitude: 4.478\({}^{\circ}\) W; elevation: 60 m). The main data are summarized in Table 12. The measurement equipment acquires simultaneously the \(I\)-\(V\) curves, the in-plane irradiance (\(G\)) and the PV module temperature (\(T\)).  
Figure 18 shows the effect of the induced \(R_{s}\) degradation on the \(I\)-\(V\) curves under the same environmental conditions. We assume that the acquired \(I\)-\(V\) curves with \(\Delta R_{s}=0\) correspond to the healthy conditions. The red points on the curves are the only values passed to the ANN together with \(G\) and \(T\) for estimating the degraded SDM parameters.  
The ANN is trained only by using the healthy \(I\)-\(V\) curves in combination with the single-diode model for emulating the degraded curves, as described in the flowchart of Figure 3.  
The experimental degraded curves are obtained with \(\Delta R_{s}=300\) m\(\Omega\), \(\Delta R_{s}=1\)\(\Omega\) and \(\Delta R_{s}=1.5\)\(\Omega\). For the healthy conditions, \(R_{s}=364\) m\(\Omega\); thus, the induced degradation is 82%, 274%, and 412%, respectively. The SDM parameters variations estimated with the proposed ANN architecture are reported in Figure 19 for different irradiance conditions. In that figure, the \(R_{s}\) parameter shows a trend that is in agreement with the expected  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Maximum Output Power at STC [W] & \(P_{\mathrm{mpp}}\) & 53 \\ Voltage at Maximum Power [V] & \(V_{\mathrm{mpp}}\) & 17.4 \\ Current at Maximum Power [A] & \(I_{\mathrm{mpp}}\) & 3.05 \\ Short Circuit Current at STC [A] & \(I_{\mathrm{SC}}\) & 3.27 \\ Open Circuit Voltage at STC [V] & \(V_{\mathrm{OC}}\) & 21.6 \\ \(I_{\mathrm{SC}}\) temperature coefficient [A K\({}^{-1}\)] & \(\alpha\) & 0.001326 \\ \(V_{\mathrm{OC}}\) temperature coefficient [V K\({}^{-1}\)] & \(\beta\) & \(-\)0.07704 \\ Number of Cells in series & \(N_{s}\) & 36 \\ Cell Type & \(sc\)-\(Si\) & mono \\ Cell Area (cm\({}^{2}\)) & \(A_{c}\) & 104.4 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Isofton I-53 main specifications.  
Figure 18: Experimental \(I\)-\(V\) curves with added series resistance for the same environmental conditions.  
values. The saturation current is slightly changing only at high irradiance, and thus, cannot be associated to a permanent degradation effect; the other parameters do not exhibit a significant variation with respect to the values estimated for healthy conditions. It is worth noting that the variation of \(I_{sat}\) for high irradiance values, that is, not associated to a real degradation effect, could be due to the limited dataset used to train the ANN. Indeed, only 75 healthy experimental \(I\)-\(V\) curves are available for this experimental example, and they are not enough to cover all the operating conditions. An exhaustive experimental campaign should be executed that could lead to further improvement of the performance of the proposed method. Nevertheless, also for this reduced dataset, the proposed approach is able to isolate the main degradation effects by using the SDM parameters estimation as indicators of possible faults that could occur in the PV modules.