Electrical Defects Research Articles

АНАЛІЗ ЗАСТОСУВАННЯ ЗГОРТКОВИХ НЕЙРОННИХ МЕРЕЖ ДЛЯ ПІДВИЩЕННЯ НАДІЙНОСТІ У РОБОТІ ФЕС

This work is dedicated to the study of applying convolutional neural networks (CNN) for automatic recognition of photovoltaic panel conditions based on images. The focus is placed on the impact of CNN hyperparameter tuning, such as the number of layers, kernel size, learning rate, and activation function, on the accuracy of classi-fying various panel conditions, including physical damage, electrical defects, dust contamination, and clean pan-els. The use of CNNs is driven by their ability to automatically detect complex spatial patterns in images, which is crucial for accurately identifying different types of defects and anomalies on the panels. The main challenges include the variability in panel conditions and external factors, such as weather conditions, that affect data quali-ty. The influence of CNN hyperparameters on classification accuracy was analyzed, and their optimal values were determined to achieve high model accuracy. The relevance of the research lies in the growing role of CNNs in monitoring photovoltaic panel conditions, which enables timely defect detection and optimization of mainte-nance. The results demonstrate that proper hyperparameter tuning of CNNs significantly improves classification accuracy, contributing to increased efficiency and stability of photovoltaic stations. The paper emphasizes the importance of using neural networks for image analysis in the field of photovoltaic system maintenance, ensuring their effective integration into energy networks. The goal of the study is to improve the accuracy of photovoltaic panel condition classification by developing and tuning CNN models with optimal hyperparameters, enabling timely detection of panel defects and enhancing the efficiency of photovoltaic system maintenance.

Efficient and stable perovskite solar cells based on multi-active sites 5-amino-1,3,4-thiadiazole-2-thiol modified interface

The highest certification efficiency of perovskite solar cells (PSCs) has reached 26.7 %. However, the high defect density on the surface of perovskite films prepared by low temperature solution method and the energy mismatch between the carrier transport layers and perovskite layer (PVK) greatly limit the performance improvement of PSCs. The introduction of passivating agent to modify the perovskite interface and grain boundary can reduce the defect density, coordinate the energy level effectively, and improve the efficiency and stability of devices. A Lewis base molecule 5-amino-1,3,4-thiadiazole-2-thiol (AMTD) with multiple active sites is introduced at the interface between PVK and hole transport layer (HTL). The electron-rich groups, such as = S, –S–, –NH2, –N on AMTD, passivate the positive electrical defects on the interface and grain boundary, and increase carrier transport efficiency. The interfacial energy level array is optimized to achieve more efficient charge transportation. In addition, the modified of AMTD has a significant protective effect on the perovskite, which inhibit the moisture erosion of in environment. Consequently, the AMTD-optimized device achieves a power conversion efficiency (PCE) of 24.13 %, compared to the efficiency of 21.62 % for pristine device. The stability of the devices is improved greatly.

Dual electric field and defect engineering induced multi-channel electron transfer for boosting photocatalytic hydrogen production on Cu2+-doped ZnMoO4/ZnIn2S4 heterojunction

Serious recombination of photogenerated carriers is the bottleneck problem of achieving high-performance photocatalytic reaction. A high efficient Cu2+-doped ZnMoO4/ZnIn2S4 (CZMOZIS) heterojunction was synthesized by hydrothermal method. The CZMOZIS heterostructure achieved hydrogen production of 11637.5 µmol g−1 within 5 h, which was 9.7 times higher than that of pure ZnIn2S4. Comprehensive characterization and theoretical calculation demonstrated that the CZMOZIS composites exhibited broad light absorption, an increased specific surface area and an optimized Gibbs free energy. The presence of oxygen vacancies in CZMOZIS composites was confirmed by X-ray photoelectron spectroscopy and high-resolution transmission electron microscopy, revealing the formation of defect levels below the conduction band (CB) of ZnMoO4. Kelvin probe force microscopy (KPFM) and Zeta potential demonstrated that the intrinsic electric field (IEF) of the CZMOZIS heterojunction was enhanced, which may be attributed to the generation of polarization electric field (PEF). The double electric field provided a robust driving force for the photogenerated carrier migration and separation. The electrons in the CB and defect levels of Cu2+-ZnMoO4 could be coupled with the holes of ZnIn2S4, thereby forming a multi-channel electron transfer. This work provides a theoretical support for promoting charge transfer to improve photocatalytic performance by dual electric field and defect engineering.

Computer Simulation and Simulation of Mechanical and Electrical Equipment based on Artificial Intelligence Algorithms

The computer in mechanical and electrical equipment can now detect equipment faults through simulation thanks to the advancement of artificial intelligence (AI) technology, which makes it convenient to monitor mechanical and electrical equipment. This paper begins with a quick introduction to Agent and the Agent system, explains how Agent is applied in the current context, then analyzes the hierarchical fault diagnostic model, identifies its flaws, and suggests improvement techniques. To increase the model’s accuracy and speed of operation, the contract net model and D-S (Dempster-Shafer) evidence theory are then incorporated. Finally, simulation experiments are used to confirm the accuracy and consistency of this model. According to the experimental findings, this optimization model runs at a pace that is noticeably faster than that of other models when subjected to the same workload, demonstrating the model’s efficacy. Models 1, 2, and 3 are put side by side to demonstrate how clearly multi-task processing may cut down on the model’s running time. In the second experiment, samples of mechanical and electrical equipment defect data are taken from two groups. The results of the comparative experiments demonstrate that the optimized model in this work can be reliable up to a maximum of 0.91 and a minimum of 0.63. It is demonstrated that the model in this work is rational by the fact that among the four types of fault prediction, the optimized model’s reliability is significantly greater than the traditional model’s. The research described in this publication therefore has some reference value for computer simulation of electromechanical equipment.

Surface-modified composites of metal–organic framework and wood-derived carbon for high-performance supercapacitors

The renewable nature, high carbon content, and unique hierarchical structure of wood-derived carbon make it an optimal self-supporting electrode for energy storage. However, the limitations in specific surface area and electrical conductivity defects pose challenges to achieving satisfactory charge storage in wood-derived carbon electrodes. Therefore, exploring diverse and effective surface strategies is crucial for enhancing the electrochemical energy storage performance. Herein, a decoration technique for enhancing aesthetic appeal involves applying a metal–organic framework (Ni/Co-MOF) containing nickel and cobalt onto the inner walls of wood tracheids. The sequential modification steps include carbonization, oxidation activation, and acid-etching. The Ni/NiO/CoO-CW-4 electrode, made by acid-etching carbonized wood (CW) doped with nickel, nickel oxide, and cobalt oxide for 4 h, has excellent surface area and pore size distribution, high graphitization degree, and exceptional conductivity. Furthermore, surface modification optimizes the surface chemistry and phase composition, resulting in a 0.8 mm thick Ni/NiO/CoO-CW-4 electrode with an exceptionally high areal capacitance of 16.76 F cm−2 at 5 mA cm−2. Meanwhile, the fabricated solid-state supercapacitor achieves an impressive energy density of 0.67 mWh cm−2 (8.38 mWh cm−3) at 2.5 mW cm−2 (31.25 mW cm−3), surpassing representative modified wood-based carbon electrodes by approximately 2–7 times. Additionally, the supercapacitor demonstrates exceptional stability, maintaining 96.21 % of capacitance even over 10,000 cycles. The parameters presented here demonstrate a significant improvement compared to those typically observed in most modified wood-derived carbon-based supercapacitors, effectively addressing common issues of low energy density and suboptimal cycling performance with wood carbon composites.

A representation learning-based approach to enhancing manufacturing quality for low-voltage electrical products

In low-voltage electrical product manufacturing, resolving quality issues is heavily reliant on engineering experience, and can be time-consuming and error-prone. Through quality management systems, a large number of historical defect cases can be consolidated for analysis along with relevant causes. However, these defect descriptions are often casually described with a mix of Chinese and English language, containing domain-specific terms. Additionally, defect product features have varieties and complex relationships. Therefore, historical defect cases have not been effectively utilized to support manufacturing quality issues. To address this challenge, this study proposes a representation learning-based approach to enhance manufacturing quality. Key research contributions include: (1) A two-stage word embedding technique based on the pre-trained language model. First, TSDAE is utilized for unsupervised pre-training on a large amount of unlabeled data. Then, Sentence-BERT is utilized for fine-tuning on a small set of labeled similar sentence pairs. This process yields a pre-trained language model specific to low-voltage electrical product defects. (2) NSHPSAGE graph embedding model based on the constructed product feature knowledge graph. We select more valuable neighboring nodes during sampling and explore different aggregation functions to enhance graph embedding performance. This model effectively aggregates product feature information into “Defect_Case” nodes, yielding graph embedding vectors. The model exhibits good Weighted-Precision and Weighted-Recall with a short training duration, and it can handle new nodes, addressing the issue of heterogeneous graph embedding. (3) A defect case recommendation technique that fuses word embedding and graph embedding. We use Multi-Head Attention Fusion in the late-fusion to obtain defect case vectors. This approach comprehensively considers defect description semantic knowledge and complex product feature relationships, enabling accurate defect case recommendation with the prototype system.

Reflection-type orientation-switchable optical differentiator exemplified by the BK7-MoS2 interface

Optical differential operation is an ultra-fast edge information extraction technology that enables identifying the image features, whereas current optical differentiators mainly operate along one or two differential orientations and is incompetent for switching the differential orientation. A reflection-type orientation-switchable optical differentiator is proposed and the BK7-MoS2 interface is used as an example to analyze the edge detection performance. Theory suggests that there are dual modulation methods, one is tuning the incident polarization angle with a Brewster incident angle, and the other one is altering the incident angle with a nearly vertical incident polarization angle. The reason is that the term r p /r s tan α ≪ 1 is an ignored small quantity under these two situations and the spatial spectral transfer function is closely related to the scale coefficients C x and C y , whose magnitudes are both the incident polarization angle and incident angle dependent. Our optical differentiator can be combined with other tuning means since some two-dimensional materials are responsive to external stimuli, such as electrical or magnetic fields and defect engineering, these findings have potential applications in optical sensing, machine vision, and biomedical imaging.

Constructing Metal-Organic Framework Films with Adjustable Electronic Properties on Hematite Photoanode for Boosting Photogenerated Carrier Transport.

Hematite (α-Fe2O3) has become a research hotspot in the field of photoelectrochemical water splitting (PEC-WS), but the low photogenerated carrier separation efficiency limits further application. The electronic structure regulation, such as element doping and organic functional groups with different electrical properties, is applied to alleviate the problems of poor electrical conductivity, interface defects, and band mismatch. Herein, α-Fe2O3 photoanodes are modified to regulate their electric structures and improve photogenerated carrier transport by the bimetallic metal-organic frameworks (MOFs), which are constructed with Fe/Ni and terephthalate (BDC) with 2-substitution of different organic functional groups (─H, ─Br, ─NO2 and ─NH2). The α-Fe2O3 photoanode loaded with FeNi-NH2BDC MOF catalyst exhibits the optimal photocurrent density (2mAcm-2) at 1.23VRHE, which is 2.33 times that of the pure α-Fe2O3 photoanode. The detailed PEC analyses demonstrate that the bimetallic synergistic effect between Fe and Ni can improve the conductivity and inhibit the photogenerated carrier recombination of α-Fe2O3 photoanodes. The ─NH2 group as an electron-donor group can effectively regulate the electron distribution and band structure of α-Fe2O3 photoanodes to prolong the lifetime of photogenerated holes, which facilitates photogenerated carrier transport and further enhances the PEC-WS performance of α-Fe2O3 photoanode.

Structure Prompt Augmented Language Model Embedding on Electrical Equipment Defect Knowledge Graph

Knowledge graphs have demonstrated significant impact in the power grid domain, facilitating various applications such as defect diagnosis and grid management. However, their reasoning capabilities have not been fully exploited. In this paper, we explore the utilization of knowledge graphs for power grid defect diagnosis. We construct an electrical equipment defect knowledge graph and predict missing links, which is also known as Knowledge Graph Completion (KGC). However, we notice the long-tail problem in electrical equipment knowledge graph. To tackle this challenge, we propose a novel text-based model named SPALME (Structure Prompt Augmented Language Model Embedding) that incorporates structural information as prompts. Our model leverages the power of pre-trained language models, allowing it to comprehend the semantic information of entities and relationships in the knowledge graph. Additionally, by integrating structural information as prompts during the learning process, our model gains a deeper understanding of the graph’s topological structure efficiently, effectively capturing intricate dependencies between grid equipments. We evaluate our approach on various datasets. The results demonstrate that our model consistently outperforms baseline methods on the majority of the datasets.

A processable high thermal conductivity epoxy composites with multi-scale particles for high-frequency electrical insulation

The solid-state transformer (SST) in the renewable energy grid is developing in the way of high voltage and high frequency, which often results in a sharp increase in heat production of the equipment and accelerates the failure of the insulating materials. Epoxy resin (EPR) is commonly used as an insulation material for SST due to its excellent electrical insulating properties, processing performance (viscosity), and low price. However, the thermal conductivity of EPR is only about 0.2 W/(m·K), which leads to poor insulating performance under high frequency and temperature. To enhance thermal conductivity, a substantial quantity of highly thermally conductive particles is incorporated into the EPR, accompanied by a severe increase in electrical insulation defects and viscosity. This study utilized a multi-scale particle-filled approach to investigate the thermal conductivity, processing characteristics, and high-frequency electrical insulation performance of composites. The composite, filled with 25 µm BN and 5 µm SiO2 particles, enhances thermal conductivity to 0.732 W/(m·K) and demonstrates superior electrical insulating properties at both 10 kHz and 20 kHz bipolar square waves (with an increase of 131.76% and 163.97% in relative EPR, respectively), as well as good processability. Meanwhile, it is found that the dielectric loss, thermal conductivity, and electric field distribution of the composite are the main factors affecting the electrical insulating properties from 10 to 20 kHz under high voltage.Graphical

Recent research and developments of degradation assessment and its diagnosis methods for solar PV plant: a review

The world is moving forward to a transition in the form of increasing the contribution of renewable energy sources in the energy sector, and among these, solar photovoltaic-based power generation is catching pace. Several factors are responsible for the lowering of outputs due to different degradation causes such as hotspots, corrosion, humidity, ultraviolet (UV) irradiation, temperature effects, dust, aging, weathering, yellowing, snail trails, discoloration, junction box failure, delamination, cracks, and faults from the solar photovoltaic (PV) plants. This paper presents a comprehensive review of the various form of degradation and their implications on solar PV power plant performance. The review has been carried out considering the different degradation causes and their identification methods in solar PV plant. Further, the analysis has been done on the basis of the earlier studies to understand the rates of degradation for various solar PV power plants in various climatic conditions. The PV technologies used in solar power plants are also responsible for the change in the performance of power plants over time; therefore, degradation based on different solar PV cell technologies is also analyzed. The visual inspection tools like thermal imaging with IR cameras help identify areas with abnormal heat patterns, indicating potential issues like cell or interconnect failures, loose electrical connections, or bypass diode malfunctions while EL cameras are used to identify low-level electrical excitation and defects such as cracks, hotspots, and cell-level degradation.

TAD boundary deletion causes PITX2-related cardiac electrical and structural defects

While 3D chromatin organization in topologically associating domains (TADs) and loops mediating regulatory element-promoter interactions is crucial for tissue-specific gene regulation, the extent of their involvement in human Mendelian disease is largely unknown. Here, we identify 7 families presenting a new cardiac entity associated with a heterozygous deletion of 2 CTCF binding sites on 4q25, inducing TAD fusion and chromatin conformation remodeling. The CTCF binding sites are located in a gene desert at 1 Mb from the Paired-like homeodomain transcription factor 2 gene (PITX2). By introducing the ortholog of the human deletion in the mouse genome, we recapitulate the patient phenotype and characterize an opposite dysregulation of PITX2 expression in the sinoatrial node (ectopic activation) and ventricle (reduction), respectively. Chromatin conformation assay performed in human induced pluripotent stem cell-derived cardiomyocytes harboring the minimal deletion identified in family#1 reveals a conformation remodeling and fusion of TADs. We conclude that TAD remodeling mediated by deletion of CTCF binding sites causes a new autosomal dominant Mendelian cardiac disorder.

Superspin chains solutions from 4D Chern-Simons theory

As a generalisation of the correspondence linking 2D integrable systems with 4D Chern-Simons (CS) gauge theory, superspin chains are realized by means of crossing electric and magnetic super line defects in the 4D CS with super gauge symmetry. The oscillator realization of Lax operators solving the RLL relations of integrability is obtained in the gauge theory by extending the notion of Levi decomposition to Lie superalgebras. Based on particular 3-gradings of Lie superalgebras, we obtain graded oscillator Lax matrices for superspin chains with internal symmetries given by A(m − 1 | n − 1), B(m | n), C(n) and D(m | n).

Modulating functional groups of GO to improve the electrochemical performance of Si/rGO anode

Silicon (Si)-based materials show attractive potentials as the anode for lithium-ion batteries (LIBs) due to their high theoretical specific capacity. For the conventional silicon/reduced graphene oxide (Si/rGO) composites, the electrical conductivity, C/O ratio, and defect structure of reduced graphene oxide (rGO) significantly impact their electrochemical properties. Herein, we use three methods to prepare graphite oxide (GO) to adjust the percentage of functional groups, which further enhance the physicochemical properties of rGO and thus improve the electrochemical properties of Si/rGO. The GOs were synthesized through Hummers method (labeled as GO-1), Hummers method with H2O modulation (labeled as GO-2), and Hummers method with H2O and H3BO3 modulation (labeled as GO-3). Due the H2O promotes the production of hydroxyl and epoxy groups, and H3BO3 forms complexes with hydroxyl groups,the ratio of hydroxyl and epoxy groups in GO-1 to GO-3 is increased. After reduction process, the reduced rGO-3 exhibits a high C/O ratio, large structural defects, and excellent electrical conductivity. Because of the excellent properties of rGO-3, as an electrode material, Si/rGO-3 exhibits excellent electrochemical performance, with the highest reversible capacity of about 780.8 mAh/g after 150 cycles at 1 A/g and a good rate performance of 657.0 mAh/g at 5 A/g. This work proves that modulation of rGO from the GO with different oxygen-containing functional groups lead to enhanced ionic conductivity, cyclic stability, and rate capability of the Si/rGO electrodes.

Simple preparation of V-doped NiCo-LDH as cathode of high energy density supercapacitor

One of the most promising cathode materials for supercapacitors is nickel-cobalt layered double hydroxide (NiCo-LDH). Doping atoms are widely used to solve the problems of poor electrical conductivity and structural defects of electrode materials. Vanadium (V) has received widespread attention due to its multiple oxidation states and low cost. In this article, we use one-step hydrothermal method to synthesize V-doped NiCo-LDH (V-NC1.5). By regulating the doping amount of V, 2V-NC1.5 exhibits superb specific capacitance of 1835 F g−1 at 1 A g−1. An asymmetric aqueous supercapacitor was assembled with 2V-NC1.5 and activated carbon. Its maximum energy density can reach 60.9 Wh kg−1 at 800 W kg−1 of power density. When the two units are connected in series, they can effectively power a white LED bulb and maintain its brightness for more than 30 min. This result provides a potential opportunity for the efficient deployment of innovative energy storage technologies in the coming years.

Fabrication of Zigzag Parylene Nanofibers in Liquid Crystals with Electric Field-Induced Defect Structures.

Liquid crystals (LCs) have been adopted to induce tunable physical properties that dynamically originated from their unique intrinsic properties responding to external stimuli, such as surface anchoring condition and applied electric field, which enables them to be the template for aligning functional guest materials. We fabricate the fiber array from the electrically modulated (in-plain) nematic LC template using the chemical vapor polymerization (CVP) method. Under an electric field, an induced defect structure with a winding number of -1/2 contains a periodic zigzag disclination line. It is known that LC defect structures can trap the guest materials, such as particles and chemicals. However, the resulting fibers grow along the LC directors, not trapped in the defects. To show the versatility of our platform, nanofibers are fabricated on patterned electrodes representing the alphabets 'CVP.' In addition, the semifluorinated moieties are added to fibers to provide a hydrophobic surface. The resultant orientation-controlled fibers will be used in controllable smart surfaces that can be used in sensors, electronics, photonics, and biomimetic surfaces.

Synthesis and characterization of hierarchical suspended carbon fiber structures decorated with carbon nanotubes

Carbon nanotubes (CNTs) and carbon microfibers (CMFs) have received significant attention due to their exceptional mechanical and electrical properties, which make them promising materials for various applications. This study introduces a novel approach to integrate CNTs and CMFs into a unified architecture by simultaneously conducting pyrolysis and chemical vapor deposition (CVD). The localized CVD of CNTs on suspended CMFs was achieved by utilizing Fe–Co nanoparticles (NPs) embedded in polyacrylonitrile (PAN) fibers as catalysts. Scanning electron microscopy and elemental analysis confirmed the formation of needle-like carbon structures on the pyrolyzed fiber surface, where carbon gases released from the pyrolyzing PAN fiber acted as the carbon source for the localized CVD. The incorporation of an additional carbon source, such as camphor vapor, significantly enhanced the growth and density of CNTs on the CMF. Various characterization techniques, including transmission electron microscopy, X-ray diffraction, Raman spectroscopy, and Atomic Force Microscopy, were employed to analyse the properties of the synthesized materials. The substantial increase in electrical conductivity upon incorporating CNTs highlights their positive influence on electrical properties and defect reduction. These characterization results highlight the potential applications of the fabricated structures in various fields, including sensors, lithium-ion electrodes, and microfabrication. In addition, the economic advantages of optimizing the process by integrating CVD with pyrolysis were assessed, revealing decreased operation time, lower energy consumption, and reduced chemical costs in comparison to conventional methods involving multiple intermediate processing steps.Graphical

A numerical analysis of the effect of layer-scale and microscopic parameters of membrane electrode assembly in proton exchange fuel cells under two-phase conditions

A multiphysics, multiphase, multiscale model of the membrane electrode assembly (MEA) of a polymer electrolyte membrane fuel cell (PEMFC) is presented. The model accounts for varying local effective transport that arise from assembly compression of the gas diffusion layer (GDL) and MEA microstructure, providing a more realistic description of heterogeneities. The model is validated against previous experimental data in terms of polarization curve, effect of inlet relative humidity on performance, and rib/channel saturation distribution. Then, a parametric analysis of layer-scale (compression and GDL thickness) and microscopic (pore radius and defect volume fraction) parameters is presented. The results show that performance can be improved by reducing compression under the rib (provided that electrical contact resistances are negligible), tailoring GDL thickness at intermediate values around 200μm, designing macroporous layer (MPL) and catalyst layer (CL) with pore sizes between 50–100nm and 0.5–1μm, respectively, and reducing large defect volume fractions in MPLs and CLs. To achieve these goals, an integral design of MEAs is necessary to minimize electrical contact resistances, cracks and defects, reduce inhomogeneous compression and produce highly porous multiscale structures with large continuum pore sizes at multiple scales.

Electrical insulator defect detection with incomplete annotations and imbalanced samples

AbstractInsulators are one of the key components in high‐voltage power systems that prevent transmission lines from grounding. Since they are exposed to different kinds of harsh environments and climates, periodic inspection is indispensable for the safety and high quality of power grid. Nowadays, unmanned aerial vehicle (UAV) inspection is more widely used, facilitating incorporation of convolutional neural network‐based detectors in the insulator detection task. However, these methods are generally based on the assumption that the image samples are balanced among different categories and possess completely ideal annotations. The problem of sample imbalance or incomplete annotation is rarely investigated in depth for insulator defect detection. Here, insulator defect detection with imbalanced data and incomplete annotations is focused on. The proposed framework, named Pi‐index, introduces positive unlabelled (PU) learning to solve the problem of incomplete annotation and designs a novel index the class prior, which is a key parameter in PU learning. Moreover, focal loss is integrated in our framework to alleviate the effect of sample imbalance. Experiment results demonstrate that the proposed framework achieves better performance than the baseline methods in situations of sample imbalance and missing annotation.

MEMS Accelerometer and Hall Sensor based Identification of Electrical and Mechanical Defects in Induction Motors and Driven Systems

MEMS Accelerometer and Hall Sensor based Identification of Electrical and Mechanical Defects in Induction Motors and Driven Systems