Thermal anomalies detection in a photovoltaic plant using artificial intelligence: Italy case studies

This paper proposes the application of artificial intelligence techniques for the identification of thermal anomalies that occur in a photovoltaic system due to malfunctions or faults, with the aim to limit the energy production losses by detecting faults at an early stage. The proposed approach is based on a Thermographic Non-Destructive Test conducted with Unmanned Aerial Vehicles equipped with a thermal imaging camera, which allows the detection of abnormal operating conditions without interrupting the normal operation of the PV system rapidly and cost-effectively. The thermographic images and videos are automatically inspected using a Convolutional Neural Network, developed by an open-source tool. The developed system was applied to 4 PV plants in northern Italy, with a total size of 1.2 MWp, detecting the layout of thermal anomalies with an accuracy ok 100% thanks to the pre-processing procedure used by the authors. The proposed methodology enables non-expert users to inspect the PV modules and results in a 98.3% reduction in manual image inspection time.

Automated summary

This paper proposes the use of artificial intelligence techniques and thermal imaging cameras for early fault detection in photovoltaic systems, allowing for rapid and cost-effective identification of thermal anomalies with 100% accuracy. The technology enables non-expert users to easily inspect PV modules and significantly reduces manual image inspection time by 98.3%.