Evaluation of Faults in a Photovoltaic Power Plant using Orthomosaics based on Aerial Infrared Thermography

Oliveira, Aline Kirsten Vidal de; Bracht, Matheus Körbes; Melo, Ana Paula; Lamberts, Roberto; Rüther, Ricardo

doi:10.1109/pvsc43889.2021.9518541

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…The localization of faults within a power plant is a challenging issue that can be addressed by creating an orthomosaic of the PV power plant [103]. Image mosaicking, also known as image stitching, is a computational technique that detects over-lapping key points in spatially subsequent photos and uses them to create a so-called panorama picture [94].…”

Section: Orthomosaickingmentioning

confidence: 99%

“…Lee and Park [104] and Zefri et al [105] used the software Pix4D capture to process thermal and visual images and create orthographic images with temperature information. Oliveira et al [103] compared the use of two software packages (DroneDeploy and ContextCapture) to create an orthomosaic of a 1 MW PV power plant. Higuchi and Babasaki [106] used the software OpenDroneMap to generate the orthographic image of a 2 MW PV power plant.…”

Section: Orthomosaickingmentioning

confidence: 99%

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Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

2022

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In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method.

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Section: Orthomosaickingmentioning

confidence: 99%

Section: Orthomosaickingmentioning

confidence: 99%

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

2022

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“…Standard IEC TS 62446-3 Edition 1.0 2017-06 recommends at least 3 cm px −1 as ground sample distance. A systematic and automated image acquisition enables the automation of the subsequent image processing and evaluation/assessment, as will be shown below [78,79].…”

Section: Data Acquisition-ir-image Recordingmentioning

confidence: 99%

Infrared imaging of photovoltaic modules: a review of the state of the art and future challenges facing gigawatt photovoltaic power stations

Buerhop‐Lutz

Bommes

Schlipf³

et al. 2022

Prog. Energy

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Thermography is a frequently used and appreciated method to detect underperforming PV modules in PV power stations. With the review, we give insights on two aspects: 1) are the developed measurement strategies highly efficient (about 1 module per second) to derive timely answers from the images for operators of multi-MWp power stations, and 2) do PV stakeholders get answers on the relevance of thermal anomalies for further decisions. Following these questions, the influence of measurement conditions, image and data collection, image evaluation as well as image assessment are discussed. From the literature it is clear that automated image acquisition with manned and unmanned aircrafts allow to capture more than 1 module per second. This makes it possible to achieve almost identical measurement conditions for the modules; however, it is documented to what extent the increase in speed is achieved at the expense of image resolution. Many image processing tools based on machine learning have been developed and show the potential for analysis of IR images and defect classification. There are different approaches to evaluating IR anomalies in terms of impact on performance, yield or degradation, of individual modules or modules in a string configuration. It is clear that the problem is very complex and multi-layered. On the one hand, information on the electrical interconnection is necessary, and on the other hand, there is a lack of sufficient and suitable data sets to adapt existing computer vision tools to PV. This is where we see the greatest need for action and further development to increase the expressiveness of IR images for PV stakeholder. We conclude with recommendations to improve the outcome of IR-images and encourage the generation of suitable public data sets of IR-footage for the development of machine learning tools.

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“…Automated planning of flight routes and fault detection in defective PV modules was studied in [20], proposing an automated algorithm for drone flight routes that eliminates the need to manually control the drones. The evaluation of faults by IR thermographybased orthomosaics is studied in [21,22], facilitating the location of faults. However, long flight times are required to create compound images and there is some spatial distortion.…”

Section: Introductionmentioning

confidence: 99%

Strategy Based on Two Stages for IR Thermographic Inspections of Photovoltaic Plants

Álvarez-Tey

García-López

2022

Applied Sciences

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Aerial infrared (IR) thermography has been implemented in recent years, proving to be a powerful and versatile technique for performing maintenance at photovoltaic (PV) plants. Its application speed and reliability using unmanned aerial vehicles (UAVs) or drones make it extremely interesting at large PV plants, due to the associated savings in time and costs. Ground-level thermographic inspection is slower and more costly to apply, although it does provide higher optical resolution, due to being conducted closer to the PV modules being inspected. Both techniques used in combination can improve the diagnosis. An IR thermography inspection strategy is proposed for PV plants based on two stages. The first stage of the inspection is aerial, enabling thermal faults to be detected and located quickly and reliably. The second stage of the inspection is done on the ground and applied only to the most relevant incidents revealed in the first stage. This inspection strategy was applied to a 100 kW PV plant, with an improved diagnosis verified via this procedure, as the ground-level inspection detects one-off thermal incidents from objects creating shade and from solar reflections. For PV modules with open circuits or open substrings, the use of one technique or another is immaterial.

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Evaluation of Faults in a Photovoltaic Power Plant using Orthomosaics based on Aerial Infrared Thermography

Cited by 7 publications

References 10 publications

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

Infrared imaging of photovoltaic modules: a review of the state of the art and future challenges facing gigawatt photovoltaic power stations

Strategy Based on Two Stages for IR Thermographic Inspections of Photovoltaic Plants

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