Machine Learning techniques implemented in IoT platform for fault detection in photovoltaic panels

Novel condition monitoring systems and data analysis methods are required to support and enhance the information obtained with supervisory control and data acquisition systems. The application of advanced conditions monitoring systems based on thermographic cameras embedded in unmanned aerial vehicles is a challenge in maintenance. This paper presents an Internet of Things platform to detect faults by analyzing thermal images acquired by aerial inspections. The combination of two artificial neural networks is applied to detect regions with faults in photovoltaic solar panels, providing high accuracy. A real case study is studied with thermograms from two different photovoltaic solar plants. The analysis is performed in the platform, and the average results obtained shows 93% of accuracy for hot spot detection.