Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images

Roberto Pierdicca, Marina Paolanti, Andrea Felicetti, Fabio Piccinini and Primo Zingaretti
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Roberto Pierdicca: Department of Civil and Building Engineering and Architecture, Università Politecnica delle Marche, 60131 Ancona, Italy
Marina Paolanti: Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
Andrea Felicetti: Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
Fabio Piccinini: Department of Civil and Building Engineering and Architecture, Università Politecnica delle Marche, 60131 Ancona, Italy
Primo Zingaretti: Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy

Energies, 2020, vol. 13, issue 24, 1-17

Abstract: Renewable energy sources will represent the only alternative to limit fossil fuel usage and pollution. For this reason, photovoltaic (PV) power plants represent one of the main systems adopted to produce clean energy. Monitoring the state of health of a system is fundamental. However, these techniques are time demanding, cause stops to the energy generation, and often require laboratory instrumentation, thus being not cost-effective for frequent inspections. Moreover, PV plants are often located in inaccessible places, making any intervention dangerous. In this paper, we propose solAIr, an artificial intelligence system based on deep learning for anomaly cells detection in photovoltaic images obtained from unmanned aerial vehicles equipped with a thermal infrared sensor. The proposed anomaly cells detection system is based on the mask region-based convolutional neural network (Mask R-CNN) architecture, adopted because it simultaneously performs object detection and instance segmentation, making it useful for the automated inspection task. The proposed system is trained and evaluated on the photovoltaic thermal images dataset, a publicly available dataset collected for this work. Furthermore, the performances of three state-of-art deep neural networks, (DNNs) including UNet, FPNet and LinkNet, are compared and evaluated. Results show the effectiveness and the suitability of the proposed approach in terms of intersection over union (IoU) and the Dice coefficient.

Keywords: unmanned aerial vehicles; photovoltaic cells inspection; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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