Abstract
AbstractIncreasing deployment of photovoltaics (PV) plants demands for cheap and fast inspection. A viable tool for this task is thermographic imaging by unmanned aerial vehicles (UAV). In this work, we develop a computer vision tool for the semi‐automatic extraction of PV modules from thermographic UAV videos. We use it to curate a dataset containing 4.3 million IR images of 107,842 PV modules from thermographic videos of seven different PV plants. To demonstrate its use for automated PV plant inspection, we train a ResNet‐50 to classify ten common module anomalies with more than 90% test accuracy. Experiments show that our tool generalizes well to different PV plants. It successfully extracts PV modules from 512 out of 561 plant rows. Failures are mostly due to an inappropriate UAV trajectory and erroneous module segmentation. Including all manual steps our tool enables inspection of 3.5 MWp to 9 MWp of PV installations per day, potentially scaling to multi‐gigawatt plants due to its parallel nature. While we present an effective method for automated PV plant inspection, we are also confident that our approach helps to meet the growing demand for large thermographic datasets for machine learning tasks, such as power prediction or unsupervised defect identification.
Highlights
Deployment of solar photovoltaics (PV) has increased exponentially in the past years
A valuable tool for defect identification in PV modules is thermographic imaging which uses a thermal IR camera to visualize defects based on their increased temperature
To speed up the inspection process thermography is typically performed by unmanned aerial vehicles (UAV) [2,3,4,5]
Summary
Deployment of solar photovoltaics (PV) has increased exponentially in the past years. It increases the amount of data as each PV module occurs in multiple consecutive video frames It further introduces perspective distortion and other artefacts, such as sun reflections, which need to be handled by the processing tool to make the images usable for downstream anomaly classification and other machine learning algorithms. In this work we develop such an image processing tool for the semi-automatic extraction and localization of PV modules in UAV thermographic videos of large-scale PV plants A tool for semi-automatic extraction and localization of PV modules in UAV thermographic videos of large-scale PV plants which can be used for automated plant inspection and to curate large datasets for downstream machine-learning tasks. A quantitative analysis of generalization ability, processing time and failure cases of our tool
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