6533b873fe1ef96bd12d57cf

RESEARCH PRODUCT

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

Vincenzo Di DioDonatella MannoGiovanni CiprianiMarzia Traverso

subject

thermal anomaliesbusiness.industryComputer sciencePhotovoltaic systemSettore ING-IND/32 - Convertitori Macchine E Azionamenti Elettriciartificial intelligenceConvolutional neural networkReduction (complexity)Identification (information)photovoltaic systeminfrared thermographyLimit (music)ThermalAutomatic detectionStage (hydrology)Artificial intelligencebusinessEnergy (signal processing)

description

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.

yearjournalcountryeditionlanguage
2021-09-072021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
https://doi.org/10.1109/eeeic/icpseurope51590.2021.9584494