Advanced inspection of photovoltaic installations by aerial triangulation and terrestrial georeferencing of thermal/visual imagery

Tsanakas, John A.; Ha, Long D.; Shakarchi, Franck Al

doi:10.1016/j.renene.2016.10.046

Cited by 101 publications

(48 citation statements)

References 17 publications

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“…Canny edge detection algorithm [18,19] and digital colour conversion algorithm [20,21] have been successfully implanted for identifying the hot spot regions and defects of the PV panels. Tsanakas [22] has developed a new thermal image characterization algorithm based on aerial triangulation and terrestrial georeferencing of thermal images. Vergura [23] has developed a new algorithm for quantifying thermal image features via computer-aided thermography for diagnosing PV panel defects.…”

Section: Introductionmentioning

confidence: 99%

Infrared Thermography Based Defects Testing of Solar Photovoltaic Panel with Fuzzy Rule-Based Evaluation

Balasubramani¹,

Thangavelu²,

Muniraj³

et al. 2020

Energies

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Infrared Thermography has been used as a tool for predictive and preventive maintenance of Photovoltaic panels. International Electrotechnical Commission provides some guidelines for using thermography to detect defects in Photovoltaic panels. However, the proposed guidelines focus only on the location of the hot spot than diagnosing the types of faults. The long-term reliability and efficiency of panels can be affected by progressive defects such as discolouring and delamination. This paper proposed the new Thermal Pixel Counting algorithm to detect the above faults based on three thermal profile index values. The real-time experimental testing was carried out using FLIR T420bx® thermal imager and results have been provided to validate the proposed method. In this work, the fuzzy rule-based classification system is proposed to automate the classification process. Fuzzy reasoning method based on a single winner rule fuzzy classifier is designed with modified rule weights by particular grade. The performance of the proposed classifier is compared with the conventional fuzzy classifier and neural network model.

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Section: Introductionmentioning

confidence: 99%

Infrared Thermography Based Defects Testing of Solar Photovoltaic Panel with Fuzzy Rule-Based Evaluation

Balasubramani¹,

Thangavelu²,

Muniraj³

et al. 2020

Energies

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“…Dotenco et al propose an approach for automatic detection and analysis of photovoltaic modules in aerial infrared images, firstly detecting the individual modules in infrared images, and then using statistical tests to detect the defective modules [15]. Tsanakas et al propose two different techniques for advanced inspection mapping of PV plants; aerial triangulation and terrestrial georeferencing, using data obtained by aerial thermography [16].…”

Section: Introductionmentioning

confidence: 99%

Analysis and characterization of PV module defects by thermographic inspection

Gallardo-Saavedra

Hernández-Callejo

Duque-Pérez

2019

Rev.Fac.Ing.Univ.Antioquia

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Being able to detect, to identify and to quantify the severity of defects that appear within photovoltaic modules is essential to constitute a reliable, efficient and safety system, avoiding energy losses, mismatches and safety issues. The main objective of this paper is to perform an in-depth, onsite study of 17,142 monocrystalline modules to detect every single existing defect, classifying them in different groups, studying the variance of the same kind of defect in different modules and the patterns of each group of thermal defects. Results can be useful in a subsequent development of a software to automatically detect if a module has an anomaly and its classification. Focusing on the results obtained, all faults detected have been classified in five different thermographic defects modes: hotspot in a cell, bypass circuit overheated, hotspot in the junction box, hotspot in the connection of the busbar to the junction box and whole module overheated. An analysis of patterns of the different defects is included, studiyng location within the module, size and temperature statistical results, as average temperature, standard deviation, maximum temperature, median and first and third quartile.

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“…(Berni et al, 2009). Recently, thermal imaging systems are used in various and wide fields of applications such as archaeological heritage documentation (Brumana et al, 2013), landslide hazard assessment (Teza et al, 2015), crop health monitoring (Mangus et al, 2016) and photovoltaic plants inspection (Tsanakas et al, 2017). Thermal mapping is getting more important in civil applications, as thermal sensors may be applied in condition that visible sensors cannot, such as foggy weather and night times which is not possible for visible cameraThermal cameras usually detect radiation in the long-infrared range of the electromagnetic spectrum (roughly 9,000-14,000 nanometers or 9-14 µm) and produce images of that radiation, called Thermograms.…”

Section: Introductionmentioning

confidence: 99%

Thermal 3d Models Enhancement Based on Integration With Visible Imagery

Javan

Savadkouhi

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In the last few years, Unmanned Aerial Vehicles (UAVs) are being frequently used to acquire high resolution photogrammetric images and consequently producing Digital Surface Models (DSMs) and orthophotos in a photogrammetric procedure for topography and surface processing applications. Thermal imaging sensors are mostly used for interpretation and monitoring purposes because of lower geometric resolution. But yet, thermal mapping is getting more important in civil applications, as thermal sensors can be used in condition that visible sensors cannot, such as foggy weather and night times which is not possible for visible cameras. But, low geometric quality and resolution of thermal images is a main drawback that 3D thermal modelling are encountered with. This study aims to offer a solution for to fixing mentioned problem and generating a thermal 3D model with higher spatial resolution based on thermal and visible point clouds integration. This integration leads to generate a more accurate thermal point cloud and DEM with more density and resolution which is appropriate for 3D thermal modelling. The main steps of this study are: generating thermal and RGB point clouds separately, registration of them in two course and fine level and finally adding thermal information to RGB high resolution point cloud by interpolation concept. Experimental results are presented in a mesh that has more faces (With a factor of 23) which leads to a higher resolution textured mesh with thermal information.

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Advanced inspection of photovoltaic installations by aerial triangulation and terrestrial georeferencing of thermal/visual imagery

Cited by 101 publications

References 17 publications

Infrared Thermography Based Defects Testing of Solar Photovoltaic Panel with Fuzzy Rule-Based Evaluation

Infrared Thermography Based Defects Testing of Solar Photovoltaic Panel with Fuzzy Rule-Based Evaluation

Analysis and characterization of PV module defects by thermographic inspection

Thermal 3d Models Enhancement Based on Integration With Visible Imagery

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