Search Results (14)

Background/Objectives: This study aims to identify genetic variants associated with early-onset inflammatory bowel disease (IBD) and to improve diagnostic and therapeutic approaches. In selected monogenic IBD cases, treatment included colchicine, interleukin-1 inhibitors, and hematopoietic stem cell transplantation. Methods: This study included patients with early-onset IBD, defined as IBD diagnosed before the age of 10, who were under follow-up at the Department of Pediatric Gastroenterology, Hacettepe University, and agreed to participate between December 2018 and April 2021. Whole-exome sequencing (WES) was performed prospectively in patients without a prior diagnosis of monogenic disease, while clinical and laboratory data were reviewed retrospectively. Identified variants were evaluated for pathogenicity using standard bioinformatics tools. Results: A total of 47 patients were enrolled, including 33 boys (70.2%) and 14 girls (29.8%). The median age at symptom onset was 36 months (IQR: 10–72), and the median age at diagnosis was 3.7 years (IQR: 1.5–7.6). Crohn’s disease was diagnosed in 53.2% (n = 25), ulcerative colitis in 38.3% (n = 18), and unclassified IBD in 8.5% (n = 4). Monogenic IBD was identified in 36.2% (n = 17) of patients, including nine with Familial Mediterranean Fever and others with glycogen storage disease type 1b (n = 2), XIAP deficiency, chronic granulomatous disease, DOCK8 deficiency, IL10 receptor alpha defect, LRBA deficiency, and NFKB2 deficiency (n = 1 each). A novel SLC29A3 gene variant (c.480_481delTGinsCA, p.V161I) (transcript ID: ENST00000479577.2) was identified in 76.6% (n = 36) of patients. Conclusions: This study underscores the importance of genetic variants in early-onset IBD, particularly MEFV and the novel NFKB2. The frequent detection of the SLC29A3 variant may suggest its potential involvement in the pathogenesis of the disease. Full article

One out of every hundred live births present with congenital heart abnormalities caused by the aberrant development of the embryonic cardiovascular system. The conserved zinc finger transcription factor proteins, which include GATA binding protein 5 (GATA5) and GATA binding protein (GATA6) play important roles in embryonic development and their inactivation may result in congenital heart defects (CHDs). In this study, we performed genotypic–phenotypic analyses in two families affected by right-sided CHD diagnosed by echocardiography imaging. Proband A presented with pulmonary valve stenosis, and proband B presented with complex CHD involving the right heart structures. For variant detection, we employed whole-genome single-nucleotide polymorphism (SNP) microarray and family-based whole-exome sequencing (WES) studies. Proband A is a full-term infant who was admitted to the neonatal intensive care unit (NICU) at five days of life for pulmonary valve stenosis (PVS). Genomic studies revealed a normal SNP microarray; however, quad WES analysis identified a novel heterozygous [Chr20:g.61041597C>G (p.Arg237Pro)] variant in the GATA5 gene. Further analysis confirmed that the novel variant was inherited from the mother but was absent in the father and the maternal uncle with a history of heart murmur. Proband B was born prematurely at 35 weeks gestation with a prenatally diagnosed complex CHD. A postnatal evaluation revealed right-sided heart defects including pulmonary atresia with intact ventricular septum (PA/IVS), right ventricular hypoplasia, tricuspid valve hypoplasia, hypoplastic main and bilateral branch pulmonary arteries, and possible coronary sinusoids. Cardiac catheterization yielded anatomy and hemodynamics unfavorable to repair. Hence, heart transplantation was indicated. Upon genomic testing, a normal SNP microarray was observed, while trio WES analysis identified a novel heterozygous [Chr18:c.1757C>T (p.Pro586Leu)] variant in the GATA6 gene. This variant was inherited from the father, who carries a clinical diagnosis of tetralogy of Fallot. These findings provide new insights into novel GATA5/6 variants, elaborate on the genotypic and phenotypic association, and highlight the critical role of GATA5 and GATA6 transcription factors in a wide spectrum of right-sided CHDs. Full article

With the widespread deployment of photovoltaic (PV) power stations, timely identification and rectification of module defects are crucial for extending service life and preserving efficiency. PV arrays, subjected to severe outside circumstances, are prone to defects exacerbated by dust accumulation, potentially leading to complex compound faults. The resemblance between individual and compound faults sometimes leads to misclassification. To address this challenge, this paper presents a novel hybrid deep learning model, ResGRU, which integrates a residual network (ResNet) with bidirectional gated recurrent units (BiGRU) to improve fault diagnostic accuracy. Additionally, a Squeeze-and-Excitation (SE) module is incorporated to enhance relevant features while suppressing irrelevant ones, hence improving performance. To further optimize inter-class separability and intra-class compactness, a center loss function is employed as an auxiliary loss to enhance the model’s discriminative capacity. This proposed method facilitates the automated extraction of fault features from I-V curves and accurate diagnosis of individual faults, partial shading scenarios, and compound faults under varying levels of dust accumulation, hence aiding in the formulation of efficient cleaning schedules. Experimental findings indicate that the suggested model achieves 99.94% accuracy on pristine data and 98.21% accuracy on noisy data, markedly surpassing established techniques such as artificial neural networks (ANN), ResNet, random forests (RF), multi-scale SE-ResNet, and other ResNet-based approaches. Thus, the model offers a reliable solution for accurate PV array fault diagnosis. Full article

The growing significance of photovoltaic (PV) monitoring systems and diagnostic methodologies is evident in their role in enhancing the power generation, efficiency, and durability of photovoltaic systems. The operational efficacy of these systems is primarily influenced by factors such as irradiation levels and cell temperature. Consequently, there exists a pressing need for dedicated scrutiny and scholarly investigation into the identification and diagnosis of defects within these systems, aiming for swift identification and rectification of failures in PV stations. This paper thus endeavors to introduce a diagnostic methodology focused on fault detection and categorization of eight types of faults occurring in shading, series resistance, shunt resistance, and bypass diode faults (disconnected, short circuited, shunted with resistor) within photovoltaic panels. This analysis employs two distinct algorithms: the initial algorithm employs the thresholding method, while the second algorithm is grounded in a Fuzzy Logic classifier (Sugeno model). Upon examination of the simulation outcomes, it becomes evident that the threshold method fails to identify all faults, necessitating the adoption of a more effective classification technique. Moreover, the Fuzzy Logic (FL) method has proven to be the most suitable approach for diagnosing PV module issues. The findings indicate that all specified faults are detectable in a discernible manner. These approaches have demonstrated proficient accuracy and efficacy in pinpointing and characterizing various faults within PV panels. Notably, our simulation endeavors were conducted utilizing Simulink/Matlab software (R2014a). Full article

In this paper, a novel hybrid model for the fault diagnosis of photovoltaic (PV) modules was developed. The model combines a convolutional neural network (CNN) with a machine learning (ML) algorithm. A total of seven defects were considered in this study: sand accumulated on PV modules, covered PV modules, cracked PV modules, degradation, dirty PV modules, short-circuited PV modules, and overheated bypass diodes. First, the hybrid CNN–ML has been developed to classify the seven common defects that occur in PV modules. Second, the developed model has been then optimized. Third, the optimized model has been implemented into a microprocessor (Raspberry Pi 4) for real-time application. Finally, a friendly graphical user interface (GUI) has been designed to help users analyze their PV modules. The proposed hybrid model was extensively evaluated by a comprehensive database collected from three regions with different climatic conditions (Mediterranean, arid, and semi-arid climates). Experimental tests showed the feasibility of such an embedded solution in the diagnosis of PV modules. A comparative study with the state-of-the-art models and our model has been also presented in this paper. Full article

In this paper, a low-cost monitoring system for an off-grid photovoltaic (PV) system, installed at an isolated location (Sahara region, south of Algeria), is designed. The PV system is used to supply a small-scale greenhouse farm. A simple and accurate fault diagnosis algorithm was developed and integrated into a low-cost microcontroller for real time validation. The monitoring system, including the fault diagnosis procedure, was evaluated under specific climate conditions. The Internet of Things (IoT) technique is used to remotely monitor the data, such as PV currents, PV voltages, solar irradiance, and cell temperature. A friendly web page was also developed to visualize the data and check the state of the PV system remotely. The users could be notified about the state of the PV system via phone SMS. Results showed that the system performs better under this climate conditions and that it can supply the considered greenhouse farm. It was also shown that the integrated algorithm is able to detect and identify some examined defects with a good accuracy. The total cost of the designed IoT-based monitoring system is around 73 euros and its average energy consumed per day is around 13.5 Wh. Full article

Nowadays, millions of photovoltaic (PV) plants are installed around the world. Given the widespread use of PV supply systems and in order to keep these PV plants safe and to avoid power losses, they should be carefully protected, and eventual faults should be detected, classified and isolated. In this paper, different machine learning (ML) and deep learning (DL) techniques were assessed for fault detection and diagnosis of PV modules. First, a dataset of infrared thermography images of normal and failure PV modules was collected. Second, two sub-datasets were built from the original one: The first sub-dataset contained normal and faulty IRT images, while the second one comprised only faulty IRT images. The first sub-dataset was used to develop fault detection models referred to as binary classification, for which an image was classified as representing a faulty PV panel or a normal one. The second one was used to design fault diagnosis models, referred to as multi-classification, where four classes (Fault1, Fault2, Fault3 and Fault4) were examined. The investigated faults were, respectively, failure bypass diode, shading effect, short-circuited PV module and soil accumulated on the PV module. To evaluate the efficiency of the investigated models, convolution matrix including precision, recall, F1-score and accuracy were used. The results showed that the methods based on deep learning exhibited better accuracy for both binary and multiclass classification while solving the fault detection and diagnosis problem in PV modules/arrays. In fact, deep learning techniques were found to be efficient for the detection and classification of different kinds of defects with good accuracy (98.71%). Through a comparative study, it was confirmed that the DL-based approaches have outperformed those based on ML-based algorithms. Full article

The current geometric increase in the global deployment of solar photovoltaic (PV) modules, both at utility-scale and residential roof-top systems, is majorly attributed to its affordability, scalability, long-term warranty and, most importantly, the continuous reduction in the levelized cost of electricity (LCOE) of solar PV in numerous countries. In addition, PV deployment is expected to continue this growth trend as energy portfolio globally shifts towards cleaner energy technologies. However, irrespective of the PV module type/material and component technology, the modules are exposed to a wide range of environmental conditions during outdoor deployment. Oftentimes, these environmental conditions are extreme for the modules and subject them to harsh chemical, photo-chemical and thermo-mechanical stress. Asides from manufacturing defects, these conditions contribute immensely to PV module’s aging rate, defects and degradation. Therefore, in recent times, there has been various investigations into PV reliability and degradation mechanisms. These studies do not only provide insight on how PV module’s performance degrades over time, but more importantly, they serve as meaningful input information for future developments in PV technologies, as well as performance prediction for better financial modelling. In view of this, prompt and efficient detection and classification of degradation modes and mechanisms due to manufacturing imperfections and field conditions are of great importance towards minimizing potential failure and associated risks. In the literature, several methods, ranging from visual inspection, electrical parameter measurements (EPM), imaging methods, and most recently data-driven techniques have been proposed and utilized to measure or characterize PV module degradation signatures and mechanisms/pathways. In this paper, we present a critical review of recent studies whereby solar PV systems performance reliability and degradation were analyzed. The aim is to make cogent contributions to the state-of-the-art, identify various critical issues and propose thoughtful ideas for future studies particularly in the area of data-driven analytics. In contrast with statistical and visual inspection approaches that tend to be time consuming and require huge human expertise, data-driven analytic methods including machine learning (ML) and deep learning (DL) models have impressive computational capacities to process voluminous data, with vast features, with reduced computation time. Thus, they can be deployed for assessing module performance in laboratories, manufacturing, and field deployments. With the huge size of PV modules’ installations especially in utility scale systems, coupled with the voluminous datasets generated in terms of EPM and imaging data features, ML and DL can learn irregular patterns and make conclusions in the prediction, diagnosis and classification of PV degradation signatures, with reduced computation time. Analysis and comparison of different models proposed for solar PV degradation are critically reviewed, in terms of the methodologies, characterization techniques, datasets, feature extraction mechanisms, accelerated testing procedures and classification procedures. Finally, we briefly highlight research gaps and summarize some recommendations for the future studies. Full article

The world’s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy sources, is a potential renewable resource to manage these enduring challenges in the energy crisis. Solar power generation is expanding globally as a result of growing energy demands and depleting fossil fuel reserves, which are presently the primary sources of power generation. In the realm of solar power generation, photovoltaic (PV) panels are used to convert solar radiation into energy. They are subjected to the constantly changing state of the environment, resulting in a wide range of defects. These defects should be discovered and remedied as soon as possible so that PV panels efficiency, endurance, and durability are not compromised. This paper focuses on five aspects, namely, (i) the various possible faults that occur in PV panels, (ii) the online/remote supervision of PV panels, (iii) the role of machine learning techniques in the fault diagnosis of PV panels, (iv) the various sensors used for different fault detections in PV panels, and (v) the benefits of fault identification in PV panels. Based on the investigated studies, recommendations for future research directions are suggested. Full article

With the rapid development of solar energy, the photovoltaic (PV) module fault detection plays an important role in knowing how to enhance the reliability of the solar photovoltaic system and knowing the fault type when a system problem occurs. Therefore, this paper proposed the hybrid algorithm of chaos synchronization detection method (CSDM) with convolutional neural network (CNN) for studying PV module fault detection. Four common PV module states were discussed, including the normal PV module, module breakage, module contact defectiveness and module bypass diode failure. First of all, the defects in 16 pieces of 20W monocrystalline silicon PV modules were preprocessed, and there were four pieces of each fault state. When the signal generator delivered high frequency voltage to the PV module, the original signal was measured and captured by the NI PXI-5105 high-speed data acquisition system (DAS) and was calculated by CSDM, to establish the chaos dynamic error map as the image feature of fault diagnosis. Finally, the CNN was employed for diagnosing the fault state of the PV module. The findings show that after entering 400 random fault data (100 data for each fault) into the proposed method for recognition, the recognition accuracy rate of the proposed method was as high as 99.5%, which is better than the traditional ENN algorithm that had a recognition rate of 86.75%. In addition, the advantage of the proposed algorithm is that the mass original measured data can be reduced by CSDM, the subtle changes in the output signals are captured effectively and displayed in images, and the PV module fault state is accurately recognized by CNN. Full article

The efficiency and profitability of photovoltaic (PV) plants are highly controlled by their operation and maintenance (O&M) procedures. Today, the effective diagnosis of any possible fault of PV plants remains a technical and economic challenge, especially when dealing with large-scale PV plants. Currently, PV plant monitoring is carried out by either electrical performance measurements or image processing. The first approach presents limited fault detection ability, it is costly and time-consuming, and it is incapable of fast identification of the physical location of the fault. In the second approach, Infrared Thermography (IRT) imaging has been used for the characterization of PV module failures, but their setup and processing are rather complex and an experienced technician is required. The use of Unmanned Aerial Vehicles (UAVs) for IRT imaging of PV plants for health status monitoring of PV modules has been identified as a cost-effective approach that offers 10–-15 fold lower inspection times than conventional techniques. However, previous works have not performed a comprehensive approach in the context of automated UAV inspection using IRT. This work provides a fully automated approach for the: (a) detection, (b) classification, and (c) geopositioning of the thermal defects in the PV modules. The system has been tested on a real PV plant in Spain. The obtained results indicate that an autonomous solution can be implemented for a full characterization of the thermal defects. Full article

Photovoltaic (PV) plant monitoring and maintenance has become an often critical activity: the high efficiency requirements of the new European policy have often been in contrast with the many low-quality plants installed in several countries over the past few years. In actual industrial practices, heterogeneous information is produced, and they are often managed in a fragmented way. Several software tools have been developed for obtaining reliable and valuable information from the PV plant’s raw data. With the aim of gathering and managing all these data in a more complex and integrated manner, an information managing system is proposed in this work—it is composed of a structured database, called the Photovoltaic Indexed Database, and a user interface, called the Digital Map, that allows for easy access and completion of the information present in the database. This information managment system and PV plant digitalization process is able to analyze and properly index the IR in the database, as well as the visual images obtained in photovoltaic plant monitoring. Full article

During their operational life, photovoltaic (PV) modules may exhibit various defects for poor sorting of electrical performance during manufacturing, mishandling during transportation and installation, and severe thermo-mechanical stresses. Electroluminescence testing and infrared thermographic imaging are the most common tests for checking these defects, but they are only economically viable for large PV plants. The defects are also manifested as abnormal electrical properties of the affected PV modules. For defect diagnosis, the appropriate parameters on their I-V curves are open circuit voltage, photo-generated current, series resistance, and the shunt resistance. The health of PV modules can be assessed by calculating these values and comparing them with the reference parameters. If these defects are diagnosed in time, the power loss is avoided and safety hazards are mitigated. This paper first presents a review of common defects in PV modules and then a review of the methods used to find the above-mentioned parameters during the normal PV operation. A simple approach to determine the resistances of the equivalent circuit is discussed. Finally, through a modification in an ordinary maximum power point tracking (MPPT) algorithm, information about the state of health of PV modules is obtained. This method is effective, especially if applied to submodule-integrated MPPT architectures. Full article

Fundamental changes concerning the development of photovoltaic (PV) installations in the Czech Republic (CR) have occurred after 2010. The limits (and subsequent termination) of support for the newly installed PV power plants (cancellation of purchase prices for produced electricity) were the most important. This change of approach was advised by the relevant state authorities before the end of the year 2010 and resulted in a massive increase in PV installations during 2010. The goal of investors was to get more favorable conditions for the purchase of the electricity produced. A considerable amount of PV installations had been registered by the end of 2010, which do not reach the projected operating performance—this is caused by errors during installation and in many cases by inappropriately used (poor quality) components. This paper is focused on the operation of PV power plants in the conditions of the CR. A final analysis of the operational measurements performed and potential approaches and methods applicable to operational diagnosis of defects on PV panels are presented. A brief mention is also made of the economic situation of PV systems operating in the current legislative conditions in the CR. Full article