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Long‐term reliability assessment of photovoltaic (PV) modules is key to ensuring the economic viability of PV systems. This paper presents a multi‐pronged performance degradation analysis of a 62.1 kWp solar PV power plant after 9.5 years of operation. For this purpose, various diagnosis techniques, namely, visual inspection, infrared thermography (IR), ultraviolet fluorescence (UVFL) and current‐voltage (I‐V) characterization, have been performed to evaluate the performance and degradation of the solar PV power plant. The visual characterization results show that the PV strings are affected by different mechanisms with different degrees of occurrences. The degradation mechanisms observed and the level of occurrence among the modules were found to be, encapsulant discolouration (100%), degraded frame adhesive (57%), degraded junction box adhesive (39%), snail trails (30%), burn marks (3%), cell cracks (2%) and delamination (0.4%). Although discolouration of the encapsulant was the most common possible degradation mechanism observed, the main causes of the power loss were snail trails and cracks in the PV cells. Furthermore, the IR thermography and UVFL analysis provided better understanding on snail trails phenomenon and crack mechanism on the affected PV modules. Besides imaging techniques, to assess the electrical performance of the system, we performed current‐voltage (I‐V) and power‐voltage (P‐V) characterization of the entire PV plant from which the degradation rates of the electrical parameters were determined. Our results show that the degradation rates for the maximum power and the fill factor are 0.84%/year and 0.66%/year, respectively, over the operating period of the installation. The estimated maximum power degradation rate for this installation could be considered as a realistic degradation rate for PV modules installed in harsh and hot desert climatic conditions. Additionally, our work has raised doubts about the validity of the warranties proposed by the manufacturers. [ABSTRACT FROM AUTHOR]

The present study introduces a novel approach employing weightless neural networks (WNN) for the detection and diagnosis of visual faults in photovoltaic (PV) modules. WNN leverages random access memory (RAM) devices to simulate the functionality of neurons. The network is trained using a flexible and efficient algorithm designed to produce consistent and precise outputs. The primary advantage of adopting WNN lies in its capacity to obviate the need for network retraining and residual generation, making it highly promising in classification and pattern recognition domains. In this study, visible faults in PV modules were captured using an unmanned aerial vehicle (UAV) equipped with a digital camera capable of capturing RGB images. The collected images underwent preprocessing and resizing before being fed as input into a pre-trained deep learning network, specifically, DenseNet-201, which performed feature extraction. Subsequently, a decision tree algorithm (J48) was employed to select the most significant features for classification. The selected features were divided into training and testing datasets that were further utilized to determine the training, test and validation accuracies of the WNN (WiSARD classifier). Hyperparameter tuning enhances WNN's performance by achieving optimal values, maximizing classification accuracy while minimizing computational time. The obtained results indicate that the WiSARD classifier achieved a classification accuracy of 100.00% within a testing time of 1.44 s, utilizing the optimal hyperparameter settings. This study underscores the potential of WNN in efficiently and accurately diagnosing visual faults in PV modules, with implications for enhancing the reliability and performance of photovoltaic systems. [ABSTRACT FROM AUTHOR]

In the thermal industry, one common way to transfer heat between hot tubes and cooling fluid is using cross-flow heat exchangers. For heat exchangers, microscale coatings are conventional safeguards for tubes from corrosion and dust accumulation. This study presents the hypothesis that incorporating domain knowledge based on governing equations can be beneficial for developing machine learning models for CFD results, given the available data. Additionally, this work proposes a novel approach for combining variables in heat exchangers and building machine learning models to forecast heat transfer in heat exchangers for turbulent flow. To develop these models, a dataset consisting of nearly 1000 cases was generated by varying different variables. The simulation results obtained from our study confirm that the proposed method would improve the coefficient of determination (R-squared) for trained models in unseen datasets. For the unseen data, the R-squared values for random forest, K-Nearest Neighbors, and support vector regression were determined to be 0.9810, 0.9037, and 0.9754, respectively. These results indicate the effectiveness and utility of our proposed model in predicting heat transfer in various types of heat exchangers. [ABSTRACT FROM AUTHOR]

Here, nitrogen doped molybdenum disulfide quantum dots (N-MoS2 QDs) are fabricated by making use of the pulsed laser ablation (PLA) process in liquid nitrogen (LN2) as a dopant agent. In fact, LN2 contributes the rapid condensation of the plasma plume to form MoS2 QDs, optimizing the conditions for the synthesis of N-doped MoS2 with p-type property. The structural/optical features of the synthesized products are studied using transmission electron microscopy (TEM), absorption spectroscopy, photoluminescence (PL) spectroscopy techniques, and X-ray photoelectron spectroscopy (XPS). The TEM image shows the creation of MoS2 QDs with 5.5 nm average size. UV-vis and PL spectroscopy confirm the formation of N-MoS2 QDs according to the dominant peaks. The Tuck plot gives a direct band-gap of 4.34 eV for MoS2 QDs. Furthermore, XPS spectroscopy reveals Mo-N bonding, indicating nitrogen doping as evidence of p-type MoS2 QDs. Thus, PLA provides a single-stage way to the clean and green synthesis of the MoS2 QDs suspension without a need for high vacuum devices and additional chemical components. Regarding the pristine MoS2, the N-MoS2 QDs benefit from a low overpotential of −0.35 V at −10 mA/cm2 per µg alongside a low Tafel slope of 300 mV/dec. Subsequently, the lower Rct value of N-MoS2 QDs verifies the enhancement of the charge transfer kinetics mainly due to the elevated electronic conductivity. Furthermore, the quasi-rectangular cyclic voltammetry (CV) as well as the larger current window demonstrate a notable electrocatalytic activity. The former is based on the enhanced active sites in favor of N-MoS2 QDs against other samples of interest. Thereby, it is discovered that the N-doped MoS2 QD acts as an effective catalyst to notably improve the performance of the hydrogen evolution reaction (HER). [ABSTRACT FROM AUTHOR]

A comprehensive understanding of failure modes of solar photovoltaic (PV) modules is key to extending their operational lifetime in the field. In this review, first, specific failure modes associated with mature PV technologies, such as crystalline silicon (c‐Si), copper indium gallium selenide (CIGS) and cadmium telluride (CdTe), are framed by sources of specific failure modes, their development from the early‐developmental stages onwards and their impact upon long term performance of PV modules. These failure modes are sorted by both PV technology and location of occurrence in PV modules, such as substrate, encapsulant, front and rear electrode, absorber and interlayers. The second part of the review is focused on emerging PV technologies, such as perovskites solar cells, dye sensitised and organic PVs, where due to their low to medium technology readiness levels, specific long‐term degradation mechanisms have not fully emerged, and most mechanisms are only partially understood. However, an in‐depth summary of the known stability challenges associated with each emerging PV technology is presented. Finally, in this paper, lessons learned from mature PV technologies are reviewed, and considerations are given in to how these might be applied to the further development of emerging technologies. Namely, any emerging PV technology must eventually pass industry‐standard qualification tests, while warranties for the lifetime of modern c‐Si‐based modules might be extended beyond the existing warranted life of 25 years. [ABSTRACT FROM AUTHOR]

Silicon, as the most abundant element in the earth's crust and the most common material used in electronic and optical equipment, has attracted the attention of many individuals to change the properties of this material, improving its electronic and optical properties. One of these efforts relies on the reduction of surface reflection by making use of different methods. However, among them, the use of lasers in creating surface microstructures has been of special importance because there is no need for masks and other additional materials. In this work, a theoretical method is utilized to analyze these textures with the theorem of diffraction grating on a micrometer scale. The surface reflection of the microstructure created by excimer laser exposure on a silicon surface is simulated. The theoretical Coordinate transformation method (C method) gives out notable results against the experimental records by approximating triangular and trapezoidal microstructures. The model is useful for predicting the reflective response of the modified microstructural morphology. One of the main applications is the texturing of the solar cell front faces to enhance their efficiency, mainly due to photon trapping. [ABSTRACT FROM AUTHOR]

At the beginning of 2022, photovoltaic (PV) installation exceeded 1 TWp which was an impressive milestone in the solar energy industry. PV system monitoring is essential to assure energy performance and the long-term reliability of PV systems. A remarkable ability of the IMS was the prediction of the output power of the PV system to increase energy yield and identify malfunctions in PV plants. The rapid recognition of failures on PV components increases the reliability and durability of PV systems. [Extracted from the article]

The optical and electrical performances of varied configurations of visually attractive mosaic cubical luminescent solar concentrator photovoltaic (LSC PV) devices have been measured. These new designs of LSC PV devices consist of four cubical lightguides containing commercially available fluorophores with edge‐ and bottom‐mounted silicon solar cells. The 1 cm3 cubical component LSC PVs are made of poly (methyl methacrylate) (PMMA) doped by dyes from 10 to 150 ppm. The lightguides are covered with bifacial silicon solar cells at their edges and/or bottom and connected in series or parallel, depending on the device configuration. In total, 19 different configurations of mosaic LSC PV devices have been experimentally evaluated in this study. [ABSTRACT FROM AUTHOR]

This paper proposes an Intelligent Monitoring System (IMS) for Photovoltaic (PV) systems using affordable and cost-efficient hardware and also lightweight software that is capable of being easily implemented in different locations and having the capability to be installed in different types of PV power plants. IMS uses the Internet of Things (IoT) platform for handling data as well as Interoperability and Communication among the devices and components in the IMS. Moreover, IMS includes a personal cloud server for computing and storing the acquired data of PV systems. The IMS also consists of a web monitor system via some open-source and lightweight software that displays the information to multiple users. The IMS uses deep ensemble models for fault detection and power prediction in PV systems. A remarkable ability of the IMS is the prediction of the output power of the PV system to increase energy yield and identify malfunctions in PV plants. To this end, a long short-term memory (LSTM) ensemble neural network is developed to predict the output power of PV systems under different environmental conditions. On the other hand, the IMS uses machine learning-based models to detect numerous faults in PV systems. The fault diagnostic of IMS is based on the following stages. Firstly, major features are elicited through an analysis of Current–Voltage (I–V) characteristic curve under different faulty and normal events. Second, an ensemble learning model including Naive Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) is used for detecting and classifying fault events. To enhance the performance in the process of fault detection, a feature selection algorithm is also applied. A PV system has been designed and implemented for testing and validating the IMS under real conditions. IMS is an interoperable, scalable, and replicable solution for holistic monitoring of PV plant from data acquisition, storing, pre-and post-processing to malfunction and failure diagnosis, performance and energy yield assessment, and output power prediction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: Sentiment classification is a field of sentiment analysis concerned with analyzing opinions, emotions, evaluations, and attitudes regarding a special topic like a product, an organization, a person, or an incident. With the growth of user‐generated content on the Web, this field gained great importance in online reviews. With a wide range of reviews, customers cannot read all reviews. Considering the increasing rate of electronic documents and the urgent need manually mine for keywords that are hard and time‐consuming, doing the same automatically is of high demand. A new framework proposed here to mine and classify users' comments based on mining keywords by applying the sequence pattern mining through the Separation‐Power concept, a multi‐objective evolutionary algorithm based on decomposition with four objectives, and a neural network as the final classifier. Some modifications are made on multi‐objective evolutionary algorithm based on decomposition and Apriori algorithms to improve the text classification efficiency. To evaluate the proposed framework, three datasets applied; which compared with the two methods to measure accuracy, precision, recall, and error‐index. The results indicate that this framework provides a better outcome than its counterparts with 99.45 precision, 99.34 accuracy, 99.48 recall, and 99.28% f‐measure. [ABSTRACT FROM AUTHOR]

Line–line (LL) and line-ground (LG) faults may not be detected by common protection devices in Photovoltaic (PV) arrays as these faults are not detectable under high fault impedance and low mismatch level. In recent years, many efforts have been devoted to overcome these challenges using intelligent methods. However, these methods could not classify the type of faults and diagnose their severity. In this article, we propose a novel and intelligent fault monitoring method to detect and classify LL and LG faults at the dc side of PV systems. For this purpose, the main features of current–voltage curves under different fault events and normal conditions are extracted. The faults are categorized using the hierarchical classification platform. Later, the LL and LG faults are detected and classified by machine learning methods. The proposed method aims to reduce the amount of dataset, which is required for the learning process, and also obtains a higher accuracy in detecting and classifying the fault events at low mismatch levels and high fault impedance compared with other fault diagnostic methods. The experimental results verify that the proposed method precisely detects and classifies LL and LG faults on PV systems under different conditions and severity with the accuracy of 96.66% and 91.66%, respectively. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) monitoring and fault detection are very crucial to enhance the service life and reliability of PV systems. It is difficult to detect and classify the faults at the Direct Current (DC) side of PV arrays by common protection devices, especially Line-to-Line (LL) faults, because such faults are not detectable under high impedance fault and low mismatch conditions. If these faults are not diagnosed, they may significantly reduce the output power of PV systems and even cause fire catastrophe. Recently, many efforts have been devoted to detecting and classifying LL faults. However, these methods could not efficiently detect and classify the LL faults under high impedance and low mismatch. This paper proposes a novel fault diagnostic scheme in accordance with the two main stages. First, the key features are extracted via analyzing Current–Voltage (I–V) characteristics under various LL fault events and normal operation. Second, a genetic algorithm (GA) is used for parameter optimization of the kernel functions used in the Support Vector Machine (SVM) classifier and feature selection in order to obtain higher performance in diagnosing the faults in PV systems. In contrast to previous studies, this method requires only a small dataset for the learning process and it has a higher accuracy in detecting and classifying the LL fault events under high impedance and low mismatch levels. The simulation results verify the validity and effectiveness of the proposed method in detecting and classifying of LL faults in PV arrays even under complex conditions. The proposed method detects and classifies the LL faults under any condition with an average accuracy of 96% and 97.5%, respectively. [ABSTRACT FROM AUTHOR]

In this study, a novel configuration for luminescent solar concentrator photovoltaic (LSC PV) devices is presented, with vertically placed bifacial PV solar cells made of mono-crystalline silicon (mono c-Si). This LSC PV device comprises multiple rectangular cuboid lightguides, made of poly (methyl methacrylate) (PMMA), containing Lumogen dyes, in particular, either Lumogen red 305 or orange 240. The bifacial solar cells are located in between these lightguide cubes and can, therefore, receive irradiance at both of their surfaces. The main aim of this study is to theoretically determine the power conversion efficiency (PCE) of five differently configured LSC PV devices. For this purpose, Monte Carlo ray tracing simulations were executed to analyze the irradiance at receiving PV cell surfaces, as well as the optical performance of these LSC PV devices. Five different LSC PV devices, with different geometries and varying dye concentrations, were modeled. To maximize the device efficiency, the bifacial cells were also attached to the back side of the lightguides. The ray tracing simulations resulted in a maximum efficiency of 16.9% under standard test conditions (STC) for a 15 × 15 cm2 LSC PV device, consisting of nine rectangular cuboid 5 × 5 × 1 cm3 PMMA lightguides with 5 ppm orange 240 dye, with 12 vertically positioned 5 × 1 cm2 bifacial cells in between the lightguides and nine 5 × 5 cm2 PV cells attached to the back of the device. If the cells are not applied to the back of this LSC PV device configuration, the maximum PCE will be 2.9% (under STC), where the LSC PV device consists of 25 cubical 1 × 1 × 1 cm3 PMMA lightguides with 110 ppm red 305 dye and 40 vertically oriented bifacial PV cells of 1 × 1 cm2 in between the lightguides. These results show the vast future potential for LSC PV technologies, with a higher performance and efficiency than the common threshold PCE for LSC PV devices of 10%. [ABSTRACT FROM AUTHOR]

In most applications of intelligent networks equipped with wireless sensors, it is not possible to charge the nodes' battery consistency and it is impossible under some conditions. Protocols designed for this type of networks should be energy efficient. The rapid consumption of battery power in wireless sensors and high power consumption in data transmission are two main challenges of this area. Nodes' clustering is a natural way of categorizing nodes close together with the aim of using related data and removing plug-in data. However, existing clustering protocols are unbalanced in the term of energy consumption. The cluster heads are not distributed equally and overload clusters (with excess load) are much shorter than under-load clusters (low load). To solve this problem, an improved cuckoo search-based clustering algorithm (ICSCAS) has been proposed in present study. Also, performance evaluation of ICSCAS and its comparison with advanced clustering schemes in terms of total energy and residual energy consumption have been represented. [ABSTRACT FROM AUTHOR]

Supporting multiple paths for concurrent data transmission is one of the most important features of the SCTP_CMT protocol. Generally, the priority of paths or best main path are not considered in this protocol; only the priority of data and communication currents are taken into account. Thus, choosing the best main path is very important. In this study, a method is introduced for the selection of path with the highest quality of service based on the features of all the lines. Prolonged data transmission time caused by failure to select optimal main path of the SCTP_CMT in dissimilar lines was reduced through the particle swarm optimization algorithm. The results of simulation in OMNET++ and MATLAB environment showed that the data transmission speed to select the main path in the proposed algorithm was decreased. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles are becoming a reliable and useful instrument in monitoring and diagnostic services in the energy field. Operation and maintenance is a crucial factor for photovoltaic (PV) plant inspection and control activities, which currently are quite hard to be performed due to manual and dispersive procedures. The automation of the monitoring techniques will become, in future, an essential task in this market, and unmanned aerial system technology can play a role in it. This paper proposes a novel, comprehensive, and innovative approach in order to automate the inspection and prognostic procedure by means of designing a control system in order to provide accurate information on operating conditions of PV plants. The system is able to perform PV systems' monitoring, diagnosis, defects and failures reconnaissance, and data processing and to propose remedial actions. In the current experimental research, only infrared radiation sensor imaging was examined to evaluate thermographic behavior of the PV modules and to build the related image processing algorithm. However, it is possible to easily extend such a procedure in order to manage a larger amount of data. First results of this work have proven that the proposed procedure is very promising being fast, cost effective, and adaptable to large PV plants, which can be controlled during their entire lifetime. [ABSTRACT FROM PUBLISHER]

This work proposes an automated reconfiguration system to manage two types of faults in any position inside the solar arrays. The faults studied are the short-circuit to ground and the open wires in the string. These faults were selected because they severely affect power production. By identifying the affected panels and isolating the faulty one, it is possible to recover part of the power loss. Among other types of faults that the system can detect and locate are: diode short-circuit, internal open-circuit, and the degradation of the internal parasitic serial resistance. The reconfiguration system can detect, locate the above faults, and switch the distributed commutators to recover most of the power loss. Moreover, the system can return automatically to the previous state when the fault has been repaired. A SIMULINK model has been built to prove this automatic system, and a simulated numerical experiment has been executed to test the system response to the faults mentioned. The results show that the recovery of power is more than 90%, and the diagnosis accuracy and sensitivity are both 100% for this numerical experiment. [ABSTRACT FROM AUTHOR]