Anchor Points Based Accurate Fault Locating in Large-Scale Photovoltaic Plants via Aerial Infrared Videos

Ying, Yuxiang; Qi, Yuhua; Rong, Liming; Men, Hong; Joo, Young Hoon

doi:10.1109/jphotov.2021.3124748

Cited by 7 publications

(1 citation statement)

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“…Most of the approach is very similar to others in the literature, but the inclusion of the IoT platform gives some novelty to this approach. Shapsough et al [4] worked on using IoT and deep learning to create a low-cost edge-based anomaly detection system. This system can remotely sense any outliers in the PV module's performance and generate respective alerts.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning-Based Detection and Segmentation of Damage in Solar Panels

Shaik,

Balasundaram,

Kakarla

et al. 2024

Automation

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Renewable energy can lead to a sustainable future and solar energy is one the primary sources of renewable energy. Solar energy is harvested mainly by photovoltaic plants. Though there are a large number of solar panels, the economic efficiency of solar panels is not that high in comparison to energy production from coal or nuclear matter. The main risk involved in solar plants is the high maintenance cost involved in maintaining the plants. To help reduce this issue, automated solutions using Unmanned Aerial Vehicles (UAVs) and satellite imagery are proposed. In this research work, we propose a novel deep learning architecture for the segmentation of solar plant aerial images, which not only helps in automated solar plant maintenance, but can also be used for the area estimation and extraction of solar panels from an image. Along with this, we also propose a transfer learning-based model for the efficient classification of solar panel damage. Solar panel damage classification has a lot of applications. It can be integrated into monitoring systems, raising alerts when there is severe damage or damage of a certain type. The adaptive UNet model with Atrous Spatial Pyramid Pooling (ASPP) module that performed the dilated convolutions that we proposed achieved an overall accuracy of 98% with a Mean Intersection-Over-Union (IoU) Score of 95% and took under a second to process an image. Our classification model using Visual Geometry Group 19 (VGG19) as the backbone for feature extraction has achieved a classification accuracy of 98% with an F1 score of 99%, thus detecting the five classes of damage, including undamaged solar panels, in an efficient manner.

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Section: Literature Reviewmentioning

confidence: 99%