Electroluminescence imaging of PV devices: camera calibration and image correction

A method for the correction of electroluminescence (EL) images of PV devices is presented. This includes a camera calibration based on focus, dark current, flat field and lens distortion as well as artefact removal, including single-time-effects and erroneous pixels. Image correction allows EL images taken with different systems or perspectives to be normalized and used for quantitative analysis. Results include an image correction of a 4x9 cell module, imaged in different perspective positions. After correction, intensity difference and positional error were found to be less than 1%, respective 2%

Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning

A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failures. By comparing the developed methods with the commonly used one, this study demonstrates that the pre-processing of images into a feature vector of statistical parameters provides a higher classification accuracy than would be obtained by raw images alone. The proposed method can autonomously detect cracks and finger failures, enabling outdoor EL inspection using a drone-mounted system for quick assessments of photovoltaic fields.</p

Photovoltaic system faults detection using fractional multiresolution signal decomposition

Introduction. In this paper, we present an innovative methodology based on fractional wavelets for detecting defects in photovoltaic systems. Photovoltaic solar systems play a key role in the transition to a low-carbon economy, but they are susceptible to various defects such as microcracks, wiring faults, and hotspots. Early detection of these anomalies is crucial to prevent energy losses and extend the lifespan of installations. Novelty of the proposed work resides in its pioneering nature, leveraging a family of fractional wavelets, with a specific emphasis on fractional Haar wavelets. This approach enhances sensitivity in anomaly detection, introducing a fresh and promising perspective to enhance the reliability of photovoltaic installations. Purpose of this study is to develop a defect detection methodology in photovoltaic systems using fractional wavelets. We aim to improve detection sensitivity with a specific focus on low-amplitude defects such as microcracks. Method. Our innovative methodology is structured around two phases. Firstly, we undertake a crucial step of filtering photovoltaic signals using fractional Haar wavelets. This preliminary phase is of paramount importance, aiming to rid signals of unwanted noise and prepare the ground for more precise defect detection. The second phase of our approach focuses on the effective detection of anomalies. We leverage the multiresolution properties of fractional wavelets, particularly emphasizing fractional Haar wavelets. This step achieves increased sensitivity, especially in the detection of low-amplitude defects. Results. By evaluating the performance of our method and comparing it with techniques based on classical wavelets, our results highlight significant superiority in the accurate detection of microcracks, wiring faults, and hotspots. These substantial advances position our approach as a promising solution to enhance the reliability and efficiency of photovoltaic installations. Practical value. These advancements open new perspectives for preventive maintenance of photovoltaic installations, contributing to strengthening the sustainability and energy efficiency of solar systems. This methodology offers a promising solution to optimize the performance of photovoltaic installations and ensure their long-term reliability. References 21, tables 3, figures 10

Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar Cells

This study provides an automatic method for detecting finger interruptions in electroluminescence (EL) images of multicrystalline solar cells. The proposed method is a supervised classification method. We obtain regions of interest (ROI) by separating the EL image to several regions. The fingers within each ROI are candidates for defect detection. We horizontally scan each ROI region and extract features from each finger pixel. In the training stage, we record a set of features which are extracted from interrupted fingers and noninterrupted fingers. These features are represented as points in a spectral embedding space produced by spectral clustering method. These points will be classified into two clusters: interrupted fingers and noninterrupted fingers. In the classification stage, we firstly detect the position of fingers in an EL image and obtain features from each finger. The set of features in each finger combined with known features in the training stage will be represented as points in the spectral embedding space and then will be classified to the cluster with nearer cluster centroid of known features. Experimental results show that the proposed method can effectively detect finger interruptions on a set of EL images of various solar cells

Segmentation of Photovoltaic Module Cells in Electroluminescence Images

High resolution electroluminescence (EL) images captured in the infrared spectrum allow to visually and non-destructively inspect the quality of photovoltaic (PV) modules. Currently, however, such a visual inspection requires trained experts to discern different kinds of defects, which is time-consuming and expensive. Automated segmentation of cells is therefore a key step in automating the visual inspection workflow. In this work, we propose a robust automated segmentation method for extraction of individual solar cells from EL images of PV modules. This enables controlled studies on large amounts of data to understanding the effects of module degradation over time-a process not yet fully understood. The proposed method infers in several steps a high-level solar module representation from low-level edge features. An important step in the algorithm is to formulate the segmentation problem in terms of lens calibration by exploiting the plumbline constraint. We evaluate our method on a dataset of various solar modules types containing a total of 408 solar cells with various defects. Our method robustly solves this task with a median weighted Jaccard index of 94.47% and an $F_1$ score of 97.54%, both indicating a very high similarity between automatically segmented and ground truth solar cell masks

Probabilistic Distance for Mixtures of Independent Component Analyzers

© 2018 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Independent component analysis (ICA) is a blind source separation technique where data are modeled as linear combinations of several independent non-Gaussian sources. The independence and linear restrictions are relaxed using several ICA mixture models (ICAMM) obtaining a two-layer artificial neural network structure. This allows for dependence between sources of different classes, and thus a myriad of multidimensional probability density functions (PDFs) can be accurate modeled. This paper proposes a new probabilistic distance (PDI) between the parameters learned for two ICA mixture models. The PDI is computed explicitly, unlike the popular Kullback-Leibler divergence (KLD) and other similar metrics, removing the need for numerical integration. Furthermore, the PDI is symmetric and bounded within 0 and 1, which enables its use as a posterior probability in fusion approaches. In this work, the PDI is employed for change detection by measuring the distance between two ICA mixture models learned in consecutive time windows. The changes might be associated with relevant states from a process under analysis that are explicitly reflected in the learned ICAMM parameters. The proposed distance was tested in two challenging applications using simulated and real data: (i) detecting flaws in materials using ultrasounds and (ii) detecting changes in electroencephalography signals from humans performing neuropsychological tests. The results demonstrate that the PDI outperforms the KLD in change-detection capabilitiesThis work was supported by the Spanish Administration and European Union under grant TEC2014-58438-R, and Generalitat Valenciana under Grant PROMETEO II/2014/032 and Grant GV/2014/034.Safont Armero, G.; Salazar Afanador, A.; Vergara Domínguez, L.; Gomez, E.; Villanueva, V. (2018). Probabilistic Distance for Mixtures of Independent Component Analyzers. IEEE Transactions on Neural Networks and Learning Systems. 29(4):1161-1173. https://doi.org/10.1109/TNNLS.2017.2663843S1161117329

Incipient Fault Diagnosis of a Grid-Connected T-Type Multilevel Inverter Using Multilayer Perceptron and Walsh Transform

Publisher Copyright: © 2023 by the authors.This article deals with fault detection and the classification of incipient and intermittent open-transistor faults in grid-connected three-level T-type inverters. Normally, open-transistor detection algorithms are developed for permanent faults. Nevertheless, the difficulty to detect incipient and intermittent faults is much greater, and appropriate methods are required. This requirement is due to the fact that over time, its repetition may lead to permanent failures that may lead to irreversible degradation. Therefore, the early detection of these failures is very important to ensure the reliability of the system and avoid unscheduled stops. For diagnosing these incipient and intermittent faults, a novel method based on a Walsh transform combined with a multilayer perceptron (MLP)-based classifier is proposed in this paper. This non-classical approach of using the Walsh transform not only allows accurate detections but is also very fast. This last characteristic is very important in these applications due to their practical implementation. The proposed method includes two main steps. First, the acquired AC currents are used by the control system and processed using the Walsh transform. This results in detailed information used to potentially identify open-transistor faults. Then, such information is processed using the MLP to finally determine whether a fault is present or not. Several experiments are conducted with different types of incipient transistor faults to create a relevant dataset.publishersversionpublishe

Inversion technique for quantitative infrared thermography evaluation of delamination defects in multilayered structures

Inverse analysis is a promising tool for quantitative evaluation offering informative model-based prediction and providing accurate reconstruction results without pre-inspections for characterization criteria. For traditional defect inverse reconstruction, a large number of parameters are required to reconstruct a complex defect, and the corresponding forward modelling simulation is very time-consuming. Such issues result in ill-posed and complex inverse reconstruction results, which further reduces its practical applicability. In this paper, we propose and experimentally validate an inversion technique for the reconstruction of complexly-shaped delamination defects in a multilayered metallic structure using signals derived from infrared thermography (IRT) testing. First, we employ a novel defect parameterization strategy based on Fourier series fitting to represent the profile of a complicated delamination defect with relatively few coefficients. Secondly, the multi-medium element modelling method is applied to enhance a FEM fast forward simulator, in order to solve the mismatching mesh issue for mesh updating during inversion. Thirdly, a deterministic inverse algorithm based on a penalty conjugate gradient algorithm is employed to realize a robust and efficient inverse analysis. By reconstructing delamination profiles with both numerically-simulated IRT signals and those obtained through laser IRT experiments, the validity, efficiency and robustness of the proposed inversion method are demonstrated for delamination defects in a double-layered plate. Based on this strategy, not only is the feasibility of the proposed method in Infrared thermography NDT validated, but the practical applicability of inversion reconstruction analysis is significantly improved