Photovoltaics Plant Fault Detection Using Deep Learning Techniques

Solar energy is the fastest-growing clean and sustainable energy source, outperforming other forms of energy generation. Usually, solar panels are low maintenance and do not require permanent service. However, plenty of problems can result in a production loss of up to ~20% since a failed panel will...

Full description

Saved in:
Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-08, Vol.14 (15), p.3728
Main Authors: Jumaboev, Sherozbek, Jurakuziev, Dadajon, Lee, Malrey
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Classification
Clean energy
Coders
Datasets
Deep learning
Drones
Electric power generation
Energy
Energy sources
Fault detection
FPN
Image segmentation
Infrared detectors
Machine learning
Maintenance
Neural networks
panel defects
Photovoltaic cells
Photovoltaics
Plantations
Power plants
PV plant
semantic segmentation
Semantics
Solar energy
Solar panels
Sustainable energy
U-Net
Unmanned aerial vehicles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Solar energy is the fastest-growing clean and sustainable energy source, outperforming other forms of energy generation. Usually, solar panels are low maintenance and do not require permanent service. However, plenty of problems can result in a production loss of up to ~20% since a failed panel will impact the generation of a whole array. High-quality and timely maintenance of the power plant will reduce the cost of its repair and, most importantly, increase the life of the power plant and the total generation of electricity. Manual monitoring of panels is costly and time-consuming on large solar plantations; moreover, solar plantations located distantly are more complicated for humans to access. This paper presents deep learning-based photovoltaics fault detection techniques using thermal images obtained from an unmanned aerial vehicle (UAV) equipped with infrared sensors. We implemented the three most accurate segmentation models to detect defective panels on large solar plantations. The models employed in this work are DeepLabV3+, Feature Pyramid Network (FPN) and U-Net with different encoder architectures. The obtained results revealed intersection over union (IoU) of 79%, 85%, 86%, and dice coefficients of 87%, 92%, 94% for DeepLabV3+, FPN, and U-Net, respectively. The implemented models showed efficient performance and proved effective to resolve these challenges.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14153728