# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 9 Conclusions  
In this paper, the MLP artificial neural network is used for isolating faults and degradation phenomena affecting photovoltaic panels. The parameters of the single diode model are used as indicators of the main degradation phenomena. The SDM parameters are strongly different from panel to panel and depend on the environmental conditions, PV technology and manufacturing process. To identify the PV degradation through the SDM parameters, the proposed method exploits two independent MLP-ANN architectures. The first one is trained to estimate the SDM parameters of the healthy PV panel for the measured environmental conditions. Since only \(G\) and \(T\) are the inputs for this MLP-ANN, it is able to reproduce the non-linear relations existing among the SDM parameters of the healthy PV panel and the environmental conditions. The second MLP is trained to estimate the SDM parameters of the PV panel in the presence of degradation phenomena affecting the \(I\)-\(V\) curve for the measured environmental conditions. This second MLP-ANN requires as inputs \(G\), \(T\) and three points of the \(I\)-\(V\) curve measured close to the MPP; thus, it estimates the SDM parameters, including the environmental and degradation effects. To isolate the degradation effect, the difference among the two MLP-ANNs is used.  
The main benefits of the proposed solution are as follows:  
* Simple ANN architectures that allows easy implementation on an embedded system.
* The ANN training process requires only experimental healthy \(I\)-\(V\) curves.  
Figure 19: ANN identification of series resistance for different induced degradation effect and for different environmental conditions.  
* Does not require the complete _I-V_ scan during the online operation since the ANN accepts as input only three experimental points measured around the MPP.  
The method is validated with simulation and experimental results showing a good agreement between induced and estimated degradation. In line with the recent expansion of IoT technologies for PV monitoring, the proposed approach represents a useful and relevant AI-based diagnosis tool that can be used to optimize operation and maintenance activities as well as enhance decision-making processes, thereby facilitating the integration of PV systems in smart grids.  
All the authors participated to the conceptualization of the proposed methodology and to the preparation of the submitted manuscript draft. Data analysis, software development and validation were performed by R.A.G.B. with the supervision of [PERSON] and P.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"Ministero dell'Universita e Ricerca\" in the frame of PRIN 2017 project: _Holistic approach to EneRgy-efficient smart nanoGRIDS_ (HEROGIRDS #2017 WA5 ZT3_003), and by FARB funds of the University of Salerno.  
The authors wish to thank [PERSON] and [PERSON] from the University of Malaga (Spain) and [PERSON] from the University of Salerno (Italy) for providing the experimental database of _I-V_ curves of PV panels in degraded conditions.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.