## 5 Conclusions  
This paper employs datasets from performance monitoring of grid-connected PV systems in order to diagnose faults. Available data from three PV systems in central Greece, which have the same type of equipment, provide the possibility to analyze already known faults with the aid of a feed-forward artificial network, aiming to quantify the effect of these faults in the statistical metrics. The Levenberg-Marquardt optimization algorithm is proven capable to perform the ANN training to predict the AC inverter's output at normal operation, with fast convergence to nRMSE well below 5%. The quantitative identification of each type of fault is employed to support a diagnostic procedure, which is under development. Three types of faults are examined: a fault in a string of an inverter, a fault in a PV panel, and near shading faults. The nRMSE values pertaining to a fault in a string were over 30%, those induced by a fault in a PV panel were 15-20%, near shading fault were 6-9%. Thus, the loss of accuracy is significant and quite noticeable at the onset of each one of the fault types examined. A second important conclusion is that percent bias may be employed as a good indicator of a possible fault in the monitoring/measurement equipment. A third important finding is that the application of an ANN trained by a specific inverter's output, may be reliably employed in checking the performance of another inverter with the same installed power, technology and located at nearby locations. Finally, a parallel application of two networks with different inputs may diagnose faults in measurement equipment, a fact that may significantly improve the fault diagnosis procedure. The limitations of the proposed approach exist in the diagnosis of faults that have smaller and more gradual impact in PV energy production. These include soiling or dust accumulation and the gradual degradation of the PV panels. Future work will include an integration of these factors in the health monitoring methodology by the exploitation of datasets covering 10 or more years of the PV system's operation. Conceptualization, E.R. and T.S. ; methodology, E.R. and T.S. ; software, E.R. ; validation, E.R. ; formal analysis, E.R. ; investigation, E.R. and T.S. ; resources, T.S. ; data curation, E.R. ; writing--original draft preparation, E.R. ; writing--review and editing, T.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Fronius IG Plus 150V-3** & \\ \hline \(\mathrm{P_{DC,MAX}}\) & W & 12,770 \\ \(\mathrm{I_{DC,MAX}}\) & A & 55.5 \\ \(\mathrm{U_{DC,MIN}}\) & V & 230 \\ \(\mathrm{U_{DC,START}}\) & V & 260 \\ \(\mathrm{U_{DC,R}}\) & V & 370 \\ \(\mathrm{U_{DC,MAX}}\) & V & 600 \\ \(\mathrm{P_{AC,R}}\) & W & 12,000 \\ \(\mathrm{I_{AC,MAX}}\) & A & 17.4 \\ \(\mathrm{U_{AC,R}}\) & V & 3-NPE 400/230 \\ Maximum efficiency \(\mathrm{n_{inv}}\) & \% & 95.9 \\ \(\mathrm{n_{inv}}\) at 5\% \(\mathrm{P_{AC,R}}\) & \% & 91.8/92.5/91.1 \\ \(\mathrm{(230V/370V/500V)}\) & \% & 91.0/94.3/93.2 \\ \(\mathrm{n_{inv}}\) at 20\% \(\mathrm{P_{AC,R}}\) & \% & 94.7/95.1/94.6 \\ \(\mathrm{n_{inv}}\) at 25\% \(\mathrm{P_{AC,R}}\) & \% & 95.1/95.3/94.7 \\ \(\mathrm{(230V/370V/500V)}\) & \% & 95.1/95.3/94.9 \\ \(\mathrm{n_{inv}}\) at 50\% \(\mathrm{P_{AC,R}}\) & \% & 95.3/95.9/95.3 \\ \(\mathrm{(230V/370V/500V)}\) & \% & 94.7/95.6/95.4 \\ \(\mathrm{n_{inv}}\) at 100\% \(\mathrm{P_{AC,R}}\) & \% & 94.0/95.2/95.1 \\ \(\mathrm{P_{DC,MAX}}\) & W & 12,770 \\ \(\mathrm{I_{DC,MAX}}\) & A & 55.5 \\ \(\mathrm{U_{DC,MIN}}\) & V & 230 \\ \(\mathrm{U_{DC,START}}\) & V & 260 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._Figure A1: Efficiency curve of inverter with the AC Power for different DC voltages. Figure A2: General layout of PV systems.