Thermal anomalies detection in a photovoltaic plant using artificial intelligence: Italy case studies
| Autor: | Vincenzo Di Dio, Donatella Manno, Giovanni Cipriani, Marzia Traverso |
|---|---|
| Přispěvatelé: | Cipriani G., Manno D., Di Dio V., Traverso M. |
| Rok vydání: | 2021 |
| Předmět: |
thermal anomalies
business.industry Computer science Photovoltaic system Settore ING-IND/32 - Convertitori Macchine E Azionamenti Elettrici artificial intelligence Convolutional neural network Reduction (complexity) Identification (information) photovoltaic system infrared thermography Limit (music) Thermal Automatic detection Stage (hydrology) Artificial intelligence business Energy (signal processing) |
| Zdroj: | 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). |
| Popis: | 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 MW p , 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. |
| Databáze: | OpenAIRE |
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