# Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems
## 4 Conclusions  
The study's objective is to conduct a thorough investigation with a view to fault detection in solar energy systems. The goal is to identify the defective panel by analyzing the thermal images in accordance with the malfunction predictions generated after using machine learning and/or artificial intelligence algorithms to interpret the real-time data collected from the fields.  
Using both image processing and real-time inverter data analysis techniques, PV panel problems--particularly hotspot faults and bypass diode failures--that are commonly observed in solar power plants were detected. Drones were used to collect thermal images, which were then processed using a variety of algorithms to determine the panel settlement. The field's rough terrain presented the main obstacle in this situation. In the beginning, when we were developing our algorithm, we used RGB and thermal images. However, we found that our application performed better (AUC = 0.911) when it only used thermal photos. As a result, we updated our software to only use thermal images in this investigation.  
It was intended to process the recorded panel images using the Image Analytics add-in on the Orange platform, but here, with the increase in the number of images, the response time of the application increased greatly. For this reason, instead of processing all the images, all the defective panels and randomly selected non-defective panel images were used. The results showed that with the selected sample in question, a 100% accuracy rate for hotspot faults detection was achieved.  
In the last part of the study, real-time inverter data were employed for the PV fault detection. Training and test datasets were constructed using the numerical data gathered from Site-1 instantaneously. Then the data were tested with four different machine learning algorithms offered by the Orange platform. Upon reviewing the literature, it becomes apparent that over the past five years, machine learning techniques have seen extensive  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Time Stamp** **(MM/YYYY)**} & \multirow{2}{*}{**Data Size (Number of**} & \multicolumn{3}{c}{**Neural Network**} & \multicolumn{3}{c}{**Random Forest**} & \multicolumn{3}{c}{**kNN**} & \multicolumn{3}{c}{**Gradient Boosting**} \\ \cline{3-19}  & & **F1** & **Prec.** & **Rec.** & **F1** & **Prec.** & **Rec.** & **F1** & **Prec.** & **Rec.** & **F1** & **Prec.** & **Rec.** \\ \hline
03/2021 & 103,860 & 0.961 & 0.968 & 0.946 & 0.966 & 0  
utilization in research endeavors [44]. Notably, Neural Network algorithms emerge as prominent contenders within this domain with the percentage of 33.75 [45]. This is followed by the SVM method with a usage rate of 13.95%. In this study, the accuracy rates of predictions were recorded by comparing Neural Network, which is a method that has proven its maturity in the analysis of solar energy data and is accepted in the literature, with random forest, kNN, and gradient boosting algorithms, which are not frequently used in this field. The results showed that the mentioned random forest, kNN, and gradient boosting algorithms had sufficiently successful F1-score, precision, and recall rates in the field of detection of PV panel malfunctions.  
Methodology, O.B., Z.K., K.D. and O.K.; Software, O.B.; Validation, O.B.; Investigation, O.B.; Resources, K.D. and O.K.; Writing--original draft, O.B.; Writing--review & editing, Z.K.; Supervision, Z.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Authors [PERSON] and [PERSON] were employed by the company ZENA Energy. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.