# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 7 ANN Results with Simulated Degradation on _I-V_ Curves
* Calculate the \(\Delta P(k)_{mpp,\%}=\frac{P_{mpp}^{k}-P_{mpp}^{k}}{P_{mpp}^{k}}\cdot 100\)  
The percentage error evaluation allows to appreciate rapidly the ANN capability to detect the degradation on each SDM parameter and the related impact on the \(P_{mpp}\). Figure 14: ANN identification of degraded curve with 200% of variation on \(I_{sat}\) for two different irradiance and temperature conditions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Applied Degradation: \(I_{ph}^{deg}=0.95I_{ph}\) (\(-\)5\%)**} \\ \hline \(G\) [\(\frac{W}{m^{2}}\)] & \(T\) [\({}^{\circ}\)C] & & \(I_{ph}\) [A] & \(I_{sat}\) [\(\mu\)A] & \(\eta\) [-] & \(R_{s}\) [\(\Omega\)] & \(R_{sh}\) [\(\Omega\)] \\ \hline  & & \(\mathbf{P_{H}}\) & \(3.476\) & \(0.996\) & \(1.296\) & \(0.242\) & \(490\) \\ \multirow{2}{*}{695} & \multirow{2}{*}{49} & \(\mathbf{P_{deg}}\) & \(3.297\) & \(0.900\) & \(1.284\) & \(0.255\) & \(277\) \\  & & \(\mathbf{\Delta p_{\%}}\) & \(-\mathbf{5.13}\) & \(-9.6\) & \(-0.92\) & \(5.4\) & \(-43.5\) \\ \cline{3-8}  & & \(\mathbf{\Delta P_{mpp,\%}}\) & \(0.9\) & \(-\mathbf{5.37}\) & \(0.85\) & \(-0.96\) & \(-0.26\) & \(-0.77\) \\ \hline  & & \(\mathbf{P_{H}}\) & \(4.782\) & \(0.1562\) & \(1.296\) & \(0.247\) & \(466\) \\ \multirow{2}{*}{955} & \multirow{2}{*}{53} & \(\mathbf{P_{deg}}\) & \(4.526\) & \(0.1480\) & \(1.295\) & \(0.238\) & \(356\) \\  & & \(\mathbf{\Delta p_{\%}}\) & \(-\mathbf{5.36}\) & \(-5.3\) & \(-0.04\) & \(-3.8\) & \(-23.6\) \\ \cline{3-8}  & & \(\mathbf{\Delta P_{mpp,\%}}\) & \(0.9\) & \(-\mathbf{5.47}\) & \(0.39\) & \(-0.08\) & \(0.22\) & \(-0.27\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SDM parameters estimated with ANN for 1-\(V\) curves of Figure 13.  
Figure 13: ANN identification of degraded curve with \(-5\%\) of variation on \(I_{ph}\) for two different irradiance and temperature conditions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Applied Degradation:**\(\boldsymbol{\eta^{deg}=0.96\eta(-4\%)}\)} \\ \hline \(G\left[\frac{\boldsymbol{W}}{\text{m}^{2}}\right]\) & \(T\left[\text{${}^{\circ}$C}\right]\) & & \(\boldsymbol{I_{ph}[A]}\) & \(\boldsymbol{I_{sat}[\mu A]}\) & \(\boldsymbol{\eta[\text{-}]}\) & \(\boldsymbol{R_{s}[\Omega]}\) & \(\boldsymbol{R_{sh}[\Omega]}\) \\ \hline  & & \(\mathfrak{ph}\) & \(5.083\) & \(2.001\) & \(1.300\) & \(0.248\) & \(491\) \\
1020 & \(55\) & \(\mathfrak{P}_{\text{deg}}\) & \(5.090\) & \(5.730\) & \(1.256\) & \(0.254\) & \(511\) \\  & & \(\Delta\mathfrak{p}_{\%}\) & \(0.13\) & \(\boldsymbol{186}\) & \(-3.39\) & \(2.4\) & \(4.0\) \\ \cline{3-8}  & & \(\Delta\mathfrak{P}_{\text{mpp},\%}\) & \(0.9\) & \(0.14\) & \(-9.13\) & \(-3.62\) & \(-0.19\) & \(0.03\) \\ \hline  & & \(\mathfrak{ph}\) & \(2.150\) & \(0.645\) & \(1.328\) & \(0.224\) & \(520\) \\
435 & \(42\) & \(\mathfrak{P}_{\text{deg}}\) & \(2.175\) & \(1.882\) & \(1.288\) & \(0.229\) & \(559\) \\  & & \(\Delta\mathfrak{p}_{\%}\) & \(1.16\) & \(\boldsymbol{192}\) & -3.02 & \(2.1\) & \(7.4\) \\ \cline{3-8}  & & \(\Delta\mathfrak{P}_{\text{mpp},\%}\) & \(0.9\) & \(1.25\) & \(-8.68\) & \(-3.05\) & \(-0.07\) & \(0.11\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: SDM parameters estimated with ANN for _1-V_ curves of Figure 14.