Faults and infrared thermographic diagnosis in operating c-Si photovoltaic modules: A review of research and future challenges

Tsanakas, John A.; Ha, Long D.; Buerhop‐Lutz, Claudia

doi:10.1016/j.rser.2016.04.079

Cited by 287 publications

(169 citation statements)

References 51 publications

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“…Therefore, when an ideal state is considered where all the cells are identical, the same current flows through them while having the same voltage, ie they have the same short circuit current. 64 However, the disadvantage is that the thermogram does not provide a quantitative assessment of the examined module. In case of one faulty cell, the current and voltage proportions in the circuit change as given in.…”

Section: Thermographymentioning

confidence: 99%

Fault diagnosis of Photovoltaic Modules

Haque

Bharath

Khan

et al. 2019

Energy Science & Engineering

127

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Fault diagnosis and condition monitoring are important to increase the efficiency and reliability of photovoltaic modules. This paper reviews the challenges and limitations associated with fault diagnosis of solar modules. A thorough analysis of various faults responsible for failure of solar modules has been discussed. After reviewing relevant work, a monitoring tool is designed using thermography and artificial intelligent systems that allows the detection of various types of faults in PV modules and at the same time the designed tool aims to filter the nonsignificant anomalies. A neural network (NN) classifier is applied to the transfer characteristics (I‐V data) of the faulty PV module for the diagnosis which adapts multilayer perceptron (MLP) networks to identify the type and location of occurring faults. The Discrete wavelet transform (DWT) based signal processing technique is utilized in the feature extraction process to reduce the NN input size. The developed detection algorithm is adapted for 24/7 automated surveillance. For a given fault condition, the average fault detection time is observed to be <9 seconds, which is lower than the previous work done. The developed algorithm achieved 100% accuracy when tested on a predetermined fault data set.

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Section: Thermographymentioning

confidence: 99%

Fault diagnosis of Photovoltaic Modules

Haque

Bharath

Khan

et al. 2019

Energy Science & Engineering

127

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“…The power output of PV modules can be influenced by numerous factors including shading, manufacturing defects, and PV module defects due to wear and tear from the environment . In instances such as these, power output can be reduced by up to 50% of the total potential power production per module . It is therefore clear that factors negatively affecting power production should be avoided and that the early detection and classification of defects is crucial to minimise power losses.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic defect classification through thermal infrared imaging using a machine learning approach

Dunderdale

Brettenny

Clohessy

et al. 2019

Progress in Photovoltaics

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This study examines a deep learning and feature‐based approach for the purpose of detecting and classifying defective photovoltaic modules using thermal infrared images in a South African setting. The VGG‐16 and MobileNet models are shown to provide good performance for the classification of defects. The scale invariant feature transform (SIFT) descriptor, combined with a random forest classifier, is used to identify defective photovoltaic modules. The implementation of this approach has potential for cost reduction in defect classification over current methods.

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“…[24]- [26]. The digital image processing along with thermal imaging is helpful in studying the other fault effects of solar panels like cracks in cell, glass breakage, hot spots in cells, shadowing, broken ribbons, by-pass diode failure in photo-voltaic modules etc [27]. …”

Section: Operation On Matlab Digital Image Processingmentioning

confidence: 99%

Thermal Image Analysis and Segmentation to Study Temperature Effects of Cement and Bird Deposition on Surface of Solar Panels

Chaudhary¹,

Chaturvedi²

2017

IJIGSP

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Abstract-To obtain solar energy from solar photovoltaic panels is not a new task now a days. Solar panels are designed to give their maximum possible output when exposed to the solar radiations. The operating conditions of solar panels affected by the atmospheric conditions like dust, dirt, solar INSOLATION, temperature etc. Here the effect of cement and bird deposits on the surface of solar panels is considered. Temperature rise due to cement and bird deposits develop hot-spots and affects the electrical power output of the solar panel. The cement and bird deposits can be seen by visually on solar panel surface but their effect can not be visualized by naked eye. The infrared THERMOGRAPHY helps to analyze these effects by using thermal imaging camera. This paper focus to study the temperature effects of cement and bird deposition on solar panel surface by capturing thermal images along with analyzing the thermal images using MATLAB digital image processing for better understanding. Image segmentation for cement and bird deposits is performed using watershed transform to achieve the desired region of interest from thermal images.

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Faults and infrared thermographic diagnosis in operating c-Si photovoltaic modules: A review of research and future challenges

Cited by 287 publications

References 51 publications

Fault diagnosis of Photovoltaic Modules

Fault diagnosis of Photovoltaic Modules

Photovoltaic defect classification through thermal infrared imaging using a machine learning approach

Thermal Image Analysis and Segmentation to Study Temperature Effects of Cement and Bird Deposition on Surface of Solar Panels

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