3.8 Proceedings Paper

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

Publisher

IEEE
DOI: 10.1109/EEEIC/ICPSEurope51590.2021.9584494

Keywords

Automatic detection; photovoltaic system; thermal anomalies; infrared thermography; artificial intelligence

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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%.
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.

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