Electrical Detection Research Articles

Electrical fault detection and classification based on multiple machine learning algorithms

Electrical faults in the power system refer to a variety of abnormalities in the power equipment, and these faults may lead to equipment damage or even cause dangerous events such as fires and explosions, which pose a serious threat to people's lives and property. Therefore, it is very important to find and deal with these faults in time. In this paper, based on the electrical fault dataset of power system, SVM, decision tree, KNN and random forest model are used to detect electrical faults. After the confusion matrix results are analyzed, most of the predictions of the four models are correct, and only a few instances that should not be predicted as electrical faults are predicted as electrical faults. All the four machine learning models achieved more than 99% accuracy in detecting electrical faults in the power system and were able to detect electrical faults very accurately. Among them, the SVM model has the highest prediction accuracy of 99.6%, followed by the KNN model prediction accuracy of 99.5%. In summary, this paper adopts machine learning method to detect electrical faults in power systems, and achieves better results in the experimental results. This method can help power system supervisors find and deal with faults in time, and improve the safety and stability of the power system. In the future, we can further study how to optimize the model to improve the detection accuracy and apply it in practical production.

Local nano-electrode fabrication utilizing nanofluidic and nano-electrochemical control.

Miniaturized systems have attracted much attention with the recent advances in microfluidics and nanofluidics. From the capillary electrophoresis, the development of glass-based microfluidic and nanofluidic technologies has supported advances in microfluidics and nanofluidics. Most microfluidic systems, especially nanofluidic systems, are still simple, such as systems constructed with simple straight nanochannels and bulk-scale electrodes. One of the bottlenecks to the development of more complicated and sophisticated systems is to develop the locally integrated nano-electrodes. However, there are still issues with integrating nano-electrodes into nanofluidic devices because it is difficult to fit the nano-electrode size into a nanofluidic channel at the nanometer level. In this study, we propose a new method for the fabrication of local nano-electrodes in nanofluidic devices with nanofluidic and nano-electrochemistry-based experiments. An electroplating solution was introduced to a nanochannel with control of the flow and the electroplating reaction, by which nano-electrodes were successfully fabricated. In addition, a nanofluidic device was available for nanofluidic experiments with the application of 200kPa. This method can be applied to any electroplating material such as gold and copper. The local nano-electrode will make a significant contribution to the development of more complicated and sophisticated nanofluidic electrophoresis systems and to local electric detection methods for various nanofluidic devices.

Electric theft detection based on multi-source heterogeneous time series GRU network

Abstract Electricity theft has long been one of the major problems faced by power supply enterprises. To improve the robustness and accuracy of power theft detection, this article explores the method of multi-source heterogeneous time series feature fusion and designs a gated cyclic unit network model that adapts to its feature fusion. Firstly, through correlation analysis, it is verified that there is a logical correlation between different time series features and classification features, providing a theoretical basis for feature fusion. Then, an encoder decoder model framework is constructed with an attention mechanism to achieve effective fusion and state detection of user multi-source time series features. The experimental results show that compared to a single data source, the fusion of multi-source features can significantly improve detection performance, and the designed model is superior to the control model. This study provides a reference for constructing an efficient power theft detection system and also provides examples of multi-source heterogeneous feature fusion in related fields.

Biosensors for Public Health and Environmental Monitoring: The Case for Sustainable Biosensing.

Climate change is a profound crisis that affects every aspect of life, including public health. Changes in environmental conditions can promote the spread of pathogens and the development of new mutants and strains. Early detection is essential in managing and controlling this spread and improving overall health outcomes. This perspective article introduces basic biosensing concepts and various biosensors, including electrochemical, optical, mass-based, nano biosensors, and single-molecule biosensors, as important sustainability and public health preventive tools. The discussion also includes how the sustainability of a biosensor is crucial to minimizing environmental impacts and ensuring the long-term availability of vital technologies and resources for healthcare, environmental monitoring, and beyond. One promising avenue for pathogen screening could be the electrical detection of biomolecules at the single-molecule level, and some recent developments based on single-molecule bioelectronics using the Scanning Tunneling Microscopy-assisted break junctions (STM-BJ) technique are shown here. Using this technique, biomolecules can be detected with high sensitivity, eliminating the need for amplification and cell culture steps, thereby enhancing speed and efficiency. Furthermore, the STM-BJ technique demonstrates exceptional specificity, accurately detects single-base mismatches, and exhibits a detection limit essentially at the level of individual biomolecules. Finally, a case is made here for sustainable biosensors, how they can help, the paradigm shift needed to achieve them, and some potential applications.

Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection.

This paper explores a data augmentation approach for images of rigid bodies, particularly focusing on electrical equipment and analogous industrial objects. By leveraging manufacturer-provided datasheets containing precise equipment dimensions, we employed straightforward algorithms to generate synthetic images, permitting the expansion of the training dataset from a potentially unlimited viewpoint. In scenarios lacking genuine target images, we conducted a case study using two well-known detectors, representing two machine-learning paradigms: the Viola-Jones (VJ) and You Only Look Once (YOLO) detectors, trained exclusively on datasets featuring synthetic images as the positive examples of the target equipment, namely lightning rods and potential transformers. Performances of both detectors were assessed using real images in both visible and infrared spectra. YOLO consistently demonstrates F1 scores below 26% in both spectra, while VJ's scores lie in the interval from 38% to 61%. This performance discrepancy is discussed in view of paradigms' strengths and weaknesses, whereas the relatively high scores of at least one detector are taken as empirical evidence in favor of the proposed data augmentation approach.

An Improved YOLOv8 Network for Detecting Electric Pylons Based on Optical Satellite Image.

Electric pylons are crucial components of power infrastructure, requiring accurate detection and identification for effective monitoring of transmission lines. This paper proposes an innovative model, the EP-YOLOv8 network, which incorporates new modules: the DSLSK-SPPF and EMS-Head. The DSLSK-SPPF module is designed to capture the surrounding features of electric pylons more effectively, enhancing the model's adaptability to the complex shapes of these structures. The EMS-Head module enhances the model's ability to capture fine details of electric pylons while maintaining a lightweight design. The EP-YOLOv8 network optimizes traditional YOLOv8n parameters, demonstrating a significant improvement in electric pylon detection accuracy with an average mAP@0.5 value of 95.5%. The effective detection of electric pylons by the EP-YOLOv8 demonstrates its ability to overcome the inefficiencies inherent in existing optical satellite image-based models, particularly those related to the unique characteristics of electric pylons. This improvement will significantly aid in monitoring the operational status and layout of power infrastructure, providing crucial insights for infrastructure management and maintenance.

Electrical detection of mobile skyrmions with 100% tunneling magnetoresistance in a racetrack-like device

Magnetic skyrmions are topological spin textures that are regarded as promising information carriers for next-generation spintronic memory and computing devices. For practical applications, their deterministic generation, manipulation, and efficient detection are the most critical aspects. Although the generation and manipulation of skyrmions have been extensively studied, efficient electrical detection of mobile skyrmions by using techniques that are compatible with modern magnetic memory technology, remains to be adequately addressed. Here, through integrating magnetic multilayers that host nanoscale skyrmions, together with the magnetic tunnel junctions (MTJ), we demonstrate the electrical detection of skyrmions by using the tunneling magnetoresistance (TMR) effect with a TMR ratio that reaches over 100% at room temperature. By building prototype three-terminal racetrack-like devices, we further show the electrical detection of mobile skyrmions by recording the time-dependent TMR ratios. Along with many recent developments, our results could advance the development of skyrmionic memory and logic devices.

The electrical transport property and gas detection behaviour of polyvinyl alcohol and γ-Fe2O3 nanocomposite film

This paper presents the electrical and gas detection behaviour of polyvinyl alcohol (PVA)/γ-Fe2O3 nanocomposite films. The physical characterization includes X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), and UV–Visible spectroscopy. The analysis of both the dc resistivity and ac conductivity using Correlated Barrier Hoping (CBH) conduction process shows that the conductivity enhances with the decreasing of activation energy in case of the PVA/γ-Fe2O3 nanocomposite film which is the indication of interfacial defect states. The current voltage study at various temperatures follows the thermoionic emission model of Schottky Barrier diode. Moreover, the dielectric constant enhances for PVA/γ-Fe2O3 nanocomposite film, which indicates homogeneous distribution of γ-Fe2O3 nanoparticles within PVA matrix and the aggregation of trapped charges found in interface develops an interfacial polarization. As a result, the gas adsorption capacity of the nanocomposite film increases which improves the signal-to-noise ratio, leading to higher gas detection sensitivity.

Theoretical study on the effects of Mn ion doping and applied magnetic field in (In,Mn)As

Diluted magnetic semiconductors are a recent research area due to their ability to enhance ferromagnetic properties and facilitate the electrical detection of magnetoresistance and polarization. (In, Mn)As dilute magnetic semiconductor has potential application in the field of spintronic devices, such as spin field-effective transistors, spin laser light-emitted diodes, modern technology, multi-functional devices, green technology, and nanotechnology. For this study, we have considered the RKKY interaction between Mn2+ spins via delocalized carriers. The effect of manganese ion concentration and applied magnetic field on ferromagnetic diluted magnetic semiconductor properties such as dispersion relation, Curie temperature, and reduced magnetization of (In, Mn)As are studied. We have developed a spin-wave model using the Holstein-Primakaff transformation. Based on the developed model, the number of ferromagnetic magnons, dispersion relations, and Curie temperatures were calculated with or without an applied magnetic field. The reduced magnetization is also calculated. The graph of Curie temperature and magnetization of (In,Mn)As versus temperature with applied field up to 6 T and manganese ion concentration from 0.01 to 0.1 are plotted. The graph of spin wave dispersion of (In,Mn)As versus a wave vector with varying manganese ion concentration with and without an applied magnetic field up to 6 T. In this study, an InMnAs Curie temperature of 290.68 K is found without an applied magnetic field with a 0.1 manganese ion concentration, which is near room temperature. Moreover, with an applied magnetic field of 6 T at 0.1 manganese ion concentration, a Curie temperature of 342.466 K is found, which is above room temperature. Hence, these temperatures are suitable for the field of next-generation spintronic new technology.

Sensitivity enhancement using triple metal gate work function engineering of junctionless cylindrical gate all around SiNW MOSFET based biosensor for neutral biomolecule species detection for upcoming sub 14 nm technology node

In the present work Dielectric Modulation (DM) technique along with Triple Metal (TM) gate engineering has been used for the junctionless (JL) cylindrical gate all around (CGAA) Si nanowire (NW) MOSFET based biosensor for label free electrical detection of neutral biomolecules species like Uricase, Streptavidin, Protein, Biotin, ChOx (choline oxidase), and APTES etc. For this device, the Drift Diffusion approach has been used along with self-consistent solution of Schrodinger’s equation with Poisson’s equation. For the biomolecule immobilization, a nanogap cavity region is formed in the JL SiNW MOSFET by etching gate oxide layer above the SiO2 interfacial layer. The change in the drain current (ID), threshold voltage (Vth), off-current sensitivity (SIoff), threshold voltage sensitivity (SVth) and Ion/Ioff current ratio of the device have been considered as the sensing parameters for detection of biomolecules under dry environment condition. In present work a new sensing metric of sensing of biomolecules – DIBL has been added which has never been reported in literature. In this work for JL SiNW, sensitivity metrics like smaller DIBL ∼50.47 mV/V, higher SIoff9.33×105, higher SVth28.72, nearly ideal subthreshold slope (SS) ∼60 mV/dec, and higher Ion/Ioff current ratio ∼9.01 × 1012 have been obtained in comparison to available literature results.

Multifunction integration within magnetic CNT-bridged MXene/CoNi based phase change materials

Developing advanced nanocomposite phase change materials (PCMs) integrating zero-energy thermal management, microwave absorption, photothermal therapy and electrical signal detection can promote the leapfrog development of flexible wearable electronic devices. For this goal, we propose a multidimensional collaborative strategy combining two-dimensional (2D) MXene nanosheets with metal-organic framework-derived one-dimensional (1D) carbon nanotubes (CNTs) and zero-dimensional (0D) metal nanoparticles. After encapsulating paraffin wax (PW) in three-dimensional (3D) networked multidimensional MXene/CoNi–C, the resulting composite PCMs exhibit excellent thermal energy storage capacity and long-term thermally reliable stability. Benefiting from the synergistically enhanced photothermal effects of CNTs, Co/Ni nanoparticles and MXene, PW@MXene/CoNi–C can capture photons efficiently and transfer phonons quickly, yielding an ultrahigh photothermal conversion and storage efficiency of 97.5%. Additionally, PW@MXene/CoNi–C composite PCMs exhibit high microwave absorption with a minimum reflection loss of −49.3 ​dB at 8.03 ​GHz in heat-related electronic application scenarios. More attractively, the corresponding flexible phase change film can simultaneously achieve thermal management and electromagnetic shielding of electronic devices, as well as photothermal therapy and electrical signal detection for individuals. This functional integration design provides an important reference for developing advanced flexible multifunctional wearable materials and devices.

A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System

Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plants in geographically scattered grids. Electrical Power Transmission Networks (EPTN) are made of many complex, dynamic, and interrelated components. The transmission lines are essential components of the EPTN, and their fundamental duty is to transport electricity from the source area to the distribution network. These components, among others, are continually prone to electrical disturbance or failure. Hence, the EPTN required fault detection and activation of protective mechanisms in the shortest time possible to preserve stability. This research focuses on using a deep learning approach for early fault detection to improve the stability of the EPTN. Early fault detection swiftly identifies and isolates faults, preventing cascading failures and enabling rapid corrective actions. This ensures the resilience and reliability of the grid, optimizing its operation even in the face of disruptions. The design of the deep learning approach comprises a long-term and short-term memory (LSTM) model. The LSTM model is trained on an electrical fault detection dataset that contains three-phase currents and voltages at one end serving as inputs and fault detection as outputs. The proposed LSTM model has attained an accuracy of 99.65 percent with an error rate of just 1.17 percent and outperforms neural network (NN) and convolutional neural network (CNN) models.

Treefrog-Inspired Flexible Electrode with High Permeability, Stable Adhesion, and Robust Durability.

Long-term continuous monitoring (LTCM) of physiological electrical signals is an effective means for detecting several cardiovascular diseases. However, the integrated challenges of stable adhesion, low impedance, and robust durability under different skin conditions significantly hinder the application of flexible electrodes in LTCM. This paper proposes a structured electrode inspired by the treefrog web, comprising dispersed pillars at the bottom and asymmetric cone holes at the top. Attachment structures with a dispersed pillar improve the contact stability (adhesion increases 2.79/13.16 times in dry/wet conditions compared to an electrode without structure). Improved permeable duct structure provides high permeability (12 times compared to cotton). Due to high adhesion and permeability, the electrode's durability is 40 times larger than commercial Ag/AgCl electrodes. The treefrog web-like electrode has great advantages in permeability, adhesion, and durability, resulting in prospects for application in physiological electrical signal detection and a new design idea for LTCM wearable dry electrodes.

Advancing infrastructure resilience: A polymeric composite reinforcement grid with self-sensing and self-heating capabilities

In this study, a novel multifunctional grid-shaped polymeric composite (MGPC) with reinforcing, autonomous strain, stress, and damage sensing and localizing in addition to targeted heating capabilities, has been developed. Distinct from preceding composites, this grid synthesizes these features collectively for the first time and concurrently addresses current challenges in multifunctional composites, such as environmental impact, data reliability, complexity, and production costs. The fabrication process entails 3D printing an electrical circuit with conductive filaments within a polylactic acid (PLA) host polymer. The conductive filament was composed of a thermoplastic polymer (TPU) infused with carbon nanotubes (CNT)-grafted carbon fibres (CFs) produced via chemical vapour deposition. The mechanical, microstructural, and electrical properties of the grid elements and cementitious slabs reinforced with MGPC were comprehensively examined. The MGPC's performance in traffic flow monitoring, mechanical behaviour prediction, and damage localization was assessed through wheel tracking, asymmetric punch tests, piezoresistivity response evaluation, and digital image correlation techniques. Furthermore, the self-warming ability of the MGPC in cementitious composites was investigated using different voltages. The extruded TPU containing CNT-grafted CFs exhibited an electrical percolation threshold of approximately 5.0 wt%, resulting in a conductivity of around 70 S/m for the filaments. Incorporating MGPC as reinforcement within cementitious composite slabs led to notable enhancements, with flexural strength increasing by approximately 15 % and failure strain by up to 350 %. Wheel tracking tests revealed changes in the electrical: 5.8 % for 520 N and 7.8 % for 700 N wheel loads, with roughly 5.0 % average error in velocity detection. Transverse elements precisely detected wheel locations demonstrating the MGPC capabilities in accurately detecting wheel speed weight, and location. The study established strong correlations between electrical resistance changes, mechanical behaviour, and damage detection, affirming the MGPC's reliability and efficacy for damage monitoring and localization. The cementitious slab reinforced with MGPC reached around 52°C through a 20 V direct current, with a heating rate of 0.25°C/s and a power density of 142 W/m², showing its potential for practical applications such as self-healing and de-icing. However, design parameters such as mesh and conductive circuit configuration, long-term performance, as well as Life Cycle Assessment need further investigation.

SnO2/Au Microelectromechanical Systems Modified by Oxygen Vacancies for Enhanced Sensing of Dioctyl Phthalate.

Dioctyl phthalate (DOP) serves as a characteristic gas utilized in early electrical fire detection, its detection offers promising prospects for the prevention of electrical fires. In this study, we employed a modified photodeposition method to prepare Tin dioxide (SnO2) materials co-modified with Au and oxygen vacancies. Subsequently, microelectromechanical systems (MEMS) gas sensor for DOP detection were fabricated, utilizing 0.5 %Au/SnO2-I as the sensing material. Characterization results reveal the presence of abundant oxygen vacancies in 0.5 %Au/SnO2-I. The synergistic interplay of Au and oxygen vacancies resulted in a remarkable response of 9.98 to 20 ppm of DOP at operational temperature of 250 °C. This represents a significant 96 % enhancement in comparison to the response value of 4.50 exhibited by pure SnO2 at 300 °C. Notably, this gas sensor boasts low power consumption and demonstrates a quick response in the detection of overheating polyvinyl chloride (PVC) cables under simulated conditions.

Advancement of Next-Generation DNA Sequencing through Ionic Blockade and Transverse Tunneling Current Methods.

DNA sequencing is transforming the field of medical diagnostics and personalized medicine development by providing a pool of genetic information. Recent advancements have propelled solid-state material-based sequencing into the forefront as a promising next-generation sequencing (NGS) technology, offering amplification-free, cost-effective, and high-throughput DNA analysis. Consequently, a comprehensive framework for diverse sequencing methodologies and a cross-sectional understanding with meticulous documentation of the latest advancements is of timely need. This review explores a broad spectrum of progress and accomplishments in the field of DNA sequencing, focusing mainly on electrical detection methods. The review delves deep into both the theoretical and experimental demonstrations of the ionic blockade and transverse tunneling current methods across a broad range of device architectures, nanopore, nanogap, nanochannel, and hybrid/heterostructures. Additionally, various aspects of each architecture are explored along with their strengths and weaknesses, scrutinizing their potential applications for ultrafast DNA sequencing. Finally, an overview of existing challenges and future directions is provided to expedite the emergence of high-precision and ultrafast DNA sequencing with ionic and transverse current approaches.

Mechanical and electrical faults detection in induction motor across multiple sensors with CNN-LSTM deep learning model

Mechanical and electrical faults detection in induction motor across multiple sensors with CNN-LSTM deep learning model

Identification and Detection of a Peptide Biomarker and Its Enantiomer by Nanopore.

Until now, no fast, low-cost, and direct technique exists to identify and detect protein/peptide enantiomers, because their mass and charge are identical. They are essential since l- and d-protein enantiomers have different biological activities due to their unique conformations. Enantiomers have potential for diagnostic purposes for several diseases or normal bodily functions but have yet to be utilized. This work uses an aerolysin nanopore and electrical detection to identify vasopressin enantiomers, l-AVP and d-AVP, associated with different biological processes and pathologies. We show their identification according to their conformations, in either native or reducing conditions, using their specific electrical signature. To improve their identification, we used a principal component analysis approach to define the most relevant electrical parameters for their identification. Finally, we used the Monte Carlo prediction to assign each event type to a specific l- or d-AVP enantiomer.

Design, fabrication and validation of an electrical conductivity principle based two-phase detection sensor array for molten lead (Pb) based heavy metal coolants up to 600°C

Molten lead (Pb) and its alloys (PbBi and PbLi) are of immense interest for various nuclear engineering applications, including but not limited to advanced Lead-cooled Fast Reactors (LFRs), tritium Breeding Blankets (BBs) of fusion power plants and spallation targets for Accelerator-Driven Systems (ADS). Owing to their attractive thermophysical properties, these advanced fluids assert their candidacy to address the critical requirements of neutron multiplication, neutron moderation, high temperature coolants and tritium breeders, enabling the operation of next generation nuclear systems at high temperatures with better efficiencies. However, for numerous reasons such as a compromise of structural integrity at the heat transfer interface, presence of an inert cover gas during charging of molten metal in the loop, and the fusion fuel cycle itself may lead to molten metal-gas two-phase flows with high density ratios. At present, no effective diagnostics exist to detect such operational and accidental occurrences in high temperature molten metal systems resulting in a severe lack of relevant experimental studies. To address these limitations and to advance the current understanding toward two-phase regimes in high temperature Pb-based melts, the present work focuses on the design and assembly aspects of an electrical conductivity-based two-phase detection sensor array, utilizing high purity α-Al2O3 coatings with AlPO4 binder as electrical insulation layers. This paper discusses the design considerations, thermal analysis, systematic selection of structural/functional components along with preliminary results from the probe performance tests in very high temperature (600°C) static molten Pb column for real time detection of argon gas bubbles rising within the melt. Quantitative estimations of time-averaged void fraction, average bubble impaction frequency and average bubble residence time are presented from the preliminary experimental investigations.

Magnon Orbital Nernst Effect in Honeycomb Antiferromagnets without Spin-Orbit Coupling.

Recently, topological responses of magnons have emerged as a central theme in magnetism and spintronics. However, resulting Hall responses are typically weak and infrequent, since, according to present understanding, they arise from effective spin-orbit couplings, which are weaker compared to the exchange energy. Here, by investigating transport properties of magnon orbital moments, we predict that the magnon orbital Nernst effect is an intrinsic characteristic of the honeycomb antiferromagnet and therefore, it manifests even in the absence of spin-orbit coupling. For the electric detection, we propose an experimental scheme based on the magnetoelectric effect. Our results break the conventional wisdom that the Hall transport of magnons requires spin-orbit coupling by predicting the magnon orbital Nernst effect in a system without it, which leads us to envision that our work initiates the intensive search for various magnon Hall effects in generic magnetic systems with no reliance on spin-orbit coupling.