Electrical Detection Method Research Articles

Active Electric Anomaly Detection Method for Underwater Targets Based on the Orthonormal Basis Function

Electric anomaly detection (EAD) has been widely used for target detection in underwater areas. However, due to the high path loss in the water, an electric anomaly is usually submerged in environmental noise and interference, which affects the detection performance of traditional anomaly detection methods. To address this problem and improve the detection accuracy in a low signal-to-noise ratio (SNR) environment, this paper proposes an active electric anomaly detection (AEAD) method based on the orthonormal basis function (OBF). First, a four-electrode active detection system was designed. Then, a set of OBFs based on the electric field disturbance model were derived to describe the detection system characteristic, linearly and effectively. Finally, an AEAD system was designed, and the proposed method was verified experimentally using a electromagnetic simulation tool to detect a spherical anomaly target. The experimental results show that, compared with the traditional AEAD methods, the proposed method has a better performance.

Examination of Rectifier AC Side Electric Circuit Ground Fault Detection Method in DC Electric Railway Substation

There are no ground fault protection devices in the AC circuit between the rectifier transformer and rectifier of DC electric railway substations. Therefore, the ground faults in the AC electric circuit are expected to be protected by overcurrent relays or DC ground fault overvoltage relays (64P) installed in the upper system. However, these protections are inadequate, so we examined methods to detect ground faults in the AC circuit between the rectifier transformer and rectifier. Thus, it was found that the ground fault current included a third harmonic current that was absent when there were no ground faults. Therefore, we created a prototype device to extract the third harmonic current, and confirmed that ground faults in the AC electric circuit between the rectifier transformer and rectifier could be detected by artificial ground fault tests.

RES Based Islanded DC Microgrid with Enhanced Electrical Network Islanding Detection

An electrical islanding detection method for DC microgrid (MG) is proposed in this paper. Unlikely conventional AC MG system protection has been challenging for the DC MG system. The goal of the proposed scheme is to detect the islanding intelligently within the agent nodes. The proposed islanding detection technique detects the electrical link failure intelligently, and if any electrical link failure occurs, then the proposed technique maintains the MG operation and load sharing. Islanding detection is carried out using an injection signal and utilizes the existing communication network to check network connectivity. After checking the network connectivity, all the nodes maintain the look-up table of the connected network. This research work illustrates that the proposed electrical islanding detection technique is effective in maintaining the DC MG operation in the case of an electrical islanding/link failure scenario. The proposed scheme’s performance is checked through MATLAB/Simulink for the detection of islanding and maintaining the operation.

Detection of SARS-CoV-2 Virus Amplification Using a Crumpled Graphene Field-Effect Transistor Biosensor.

The rapid and unexpected spread of SARS-CoV-2 worldwide has caused unprecedented disruption to daily life and has brought forward critical challenges for public health. The disease was the largest cause of death in the United States in early 2021. Likewise, the COVID-19 pandemic has highlighted the need for rapid and accurate diagnoses at scales larger than ever before. To improve the availability of current gold standard diagnostic testing methods, the development of point-of-care devices that can maintain gold standard sensitivity while reducing the cost and providing portability is much needed. In this work, we combine the amplification capabilities of reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) techniques with high-sensitivity end-point detection of crumpled graphene field-effect transistors (cgFETs) to develop a portable detection cell. This electrical detection method takes advantage of the ability of graphene to adsorb single-stranded DNA due to noncovalent π–π bonds but not double-stranded DNA. These devices have demonstrated the ability to detect the presence of the SARS-CoV-2 virus in a range from 10 to 104 copies/μL in 20 viral transport medium (VTM) clinical samples. As a result, we achieved 100% PPV, NPV, sensitivity, and specificity with 10 positive and 10 negative VTM clinical samples. Further, the cgFET devices can differentiate between positive and negative VTM clinical samples in 35 min based on the Dirac point shift. Likewise, the improved sensing capabilities of the crumpled gFET were compared with those of the traditional flat gFET devices.

Combined application of seismic and electrical geophysical methods for karst cavities detection: A case study at the campus of the new University of Western Macedonia, Kozani, Greece

The geophysical survey presented in this paper deals with the combined application of multiple geophysical methods in the area where the new University of Wester Macedonia is to be build. The aim of the survey was to investigate the optimal methodological approach regarding the detection of karst features on limestone rocks. Electric Resistivity Tomography (ERT), Seismic Refraction Tomography (SRT) and Multichannel Analysis of Surface Waves (MASW) were applied. All measurements took place at the area of building C, where a cavity was observed on the surface. ERT was initially applied in order to delineate known features and determine a 3D geoelectric model. Seismic exploration followed in order to evaluate the potential of seismic methods in karst environment and the overall combined interpretation. The results highlighted the main advantages and disadvantages of each method, confirming the importance of joint interpretation. Seismic methods were crucial for the clarification of ambiguous ERT results caused by a resistive environment. Synthetic modeling and real data inversion proved that refraction tomography is a useful and reliable tool in cavity detection. MASW can be used as a complementary technique as it still faces challenges in the delimitation of karst features.

Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF.

It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solve the problems mentioned above. First, IGWO-ERF imports the Cosine model to nonlinearize the linearly changing convergence factor α to balance the global exploration and local exploitation capabilities of the algorithm. Then, in the later stage of the algorithm iteration, α wolf generates its mirror wolf based on the lens imaging learning strategy to increase the diversity of the population and prevent local optimum of the population. The electric pitch system fault detection method of the wind turbine generator set sets the generator power of the variable pitch system as the main state parameter. First, it uses the Pearson correlation coefficient method to eliminate the features with low correlation with the electric pitch system generator power. Then, the remaining features are ranked by the importance of the RF features. Finally, the top N features are selected to construct the electric pitch system fault data set. The data set is divided into a training set and a test set. The training set is used to train the proposed fault detection model, and the test set is used for testing. Compared with other parameter optimization algorithms, the proposed method has lower FNR and FPR in the electric pitch system fault detection of the wind turbine generator set.

Advances in multiplex electrical and optical detection of biomarkers using microfluidic devices.

Microfluidic devices can provide a versatile, cost-effective platform for disease diagnostics and risk assessment by quantifying biomarkers. In particular, simultaneous testing of several biomarkers can be powerful. Here, we critically review work from the previous 4 years up to February 2021 on developing microfluidic devices for multiplexed detection of biomarkers from samples. We focus on two principal approaches: electrical and optical detection methods that can distinguish and quantify biomarkers. Both electrical and spectroscopic multiplexed detection strategies are being employed to reach limits of detection below clinical sample levels. Some of the most promising strategies for point-of-care assays involve inexpensive materials such as paper-based microfluidic devices, or portable and accessible detectors such as smartphones. This review does not comprehensively cover all multiplexed microfluidic biomarker studies, but rather provides a critical evaluation of key work and suggests promising prospects for future advancement in this field.Graphical abstract Electrical and optical multiplexing are powerful approaches for microfluidic biomarker analysis.

Electrical and Label-Free Detection of T-Cell Activation Against COVID-19: A Method Towards Point-of-Care Monitoring of Immune System

Despite great progress in biotechnology, COVID19 has revealed the inadequacy of current healthcare technologies to contain an infectious outbreak, specifically in rapid infection recognition to facilitate containment. Wide deployment of testing methods such as viral RNA (antigen) PCR and antibody detection have provided some relief in this regard; however, current technologies still require many hours to provide results. Thus, there remains a critical need for fast, sensitive, low-cost point-of-care (POC) biosensing assays to report on the infection status and immunity of patients. Emerging data on the importance of T-cell responses in generating long-term immunity against COVID19 has highlighted T-cell activation detection as a potential way for real-time monitoring native and acquired immunity. However, current approaches for T-cell stimulation monitoring are complex, laborious, time-consuming, and costly.Here we provide proof-of-principle experiments which indicate that an electrical and label-free detection method based on CMOS-compatible silicon nanowire ion-selective field-effect transistors (SNW-ISFET) can act as a fast, sensitive, reliable, and low-cost approach to monitor T-cell response to pathogens such COVID-19. This technology enables direct detection of COVID-19-specific T-cells directly from patient blood, with potential utility as a POC diagnostic device. Detection of antigen-specific T-cell activation from clonal human T-cells lines utilizing CD8-restricted peptide-MHC complexes has been demonstrated using SNW-ISFET devices. The cell concentration was varied to achieve a clear understanding about the device sensitivity, detection limit, and reliability (Figure 1). SNW-ISFET structures were applied to detect T-cell activation in clonal T-cell lines and whole peripheral blood mononuclear cell (PBMC) with multiple cell concentrations (Figure 2). Reference experiments were applied to ensure the device reliability and sensing mechanism (Figure 3). These findings show that SNW-FETs platform are promising candidates for antigen-specific T-cell activation detection from whole blood. Figure 1

An Improved Object Detection Method for Automatic Electrical Equipment Defect Detection

Employing computer vision and machine learning on equipment defect detection has become an important trend of electric power inspection. This paper presents a new electrical equipment default detection method based on improved YOLOv3. By combining abundant geometric measures, Complete IOU(CIOU) make the bounding box regression during NMS more accurate. Focal loss function, which focus on differentiate between easy and hard examples, is employed to deal with the class imbalance problem. Experiment results show that the proposed approach obtains competitive performance compared with state-of-the-art deep learning object detection methods.

Non-invasive electric arc fault detection based on sliding approximate entropy

In recent years, electrical fires have occurred frequently and have become the first cause of various fires. Among them, the electric arc is one of the important causes of electrical fires. In this paper, the research on non-invasive electric arc fault detection is carried out for low-voltage users, which can discover and detect the electric arc only by analysing the aggregated current measurements from outdoors. In order to achieve this goal, firstly, the abrupt change of the current mode is discovered and detected with sliding window from the total load current signal based on the approximate entropy. Then the current signature samples are extracted around the abrupt change. Finally, according to the pre-set electric arc signature classifier, it is judged whether the abrupt change is caused by the electric arc fault. In addition, under laboratory conditions, an electric arc simulation experiment is carried out for common appliances of actual low-voltage users. The results show the effectiveness of the proposed non-invasive electric arc fault detection method based on approximate entropy.

Continuous MOF Membrane-Based Sensors via Functionalization of Interdigitated Electrodes

Three M-MOF-74 (M = Co, Mg, Ni) metal-organic framework (MOF) thin film membranes have been synthesized through a sensor functionalization method for the direct electrical detection of NO2. The two-step surface functionalization procedure on the glass/Pt interdigitated electrodes resulted in a terminal carboxylate group, with both steps confirmed through infrared spectroscopic analysis. This surface functionalization allowed the MOF materials to grow largely in a uniform manner over the surface of the electrode forming a thin film membrane over the Pt sensing electrodes. The growth of each membrane was confirmed through scanning electron microscopy (SEM) and X-ray diffraction analysis. The Ni and Mg MOFs grew as a continuous but non-defect free membrane with overlapping polycrystallites across the glass surface, whereas the Co-MOF-74 grew discontinuously. To demonstrate the use of these MOF membranes as an NO2 gas sensor, Ni-MOF-74 was chosen as it was consistently fabricated as the best thin and homogenous membrane, as confirmed by SEM. The membrane was exposed to 5 ppm NO2 and the impedance magnitude was observed to decrease 123× in 4 h, with a larger change in impedance and a faster response than the bulk material. Importantly, the use of these membranes as a sensor for NO2 does not require them to be defect-free, but solely continuous and overlapping growth.

An overview of various faults detection methods in synchronous generators

Synchronous generator (SG) plays a vital and critical role in the power system by supplying electric power to consumers. Various faults in SGs can cause some catastrophic events such as power disruption or blackout. These faults can be classified into two electrical and mechanical faults. Short circuit in stator windings and field winding are electrical fault while bearing, static/dynamic eccentricity, and broken damper bars faults are mechanical one. Unlike the induction machines, there are no much researches in SGs condition monitoring owing to its complex behaviour against the faults. Herein, the SG modelling approaches are presented briefly to elaborate shortcoming and challenging issues in the modelling, and then a comprehensive review of various electrical and mechanical fault detection methods is presented.

Quantifying the Window of Uncertainty for SSTDR Measurements of a Photovoltaic System

Spread spectrum time domain reflectometry (SSTDR) is a non-intrusive method for electrical fault detection and localization that enables continuous monitoring of live electrical systems. Electrical faults create changes in impedance that create subsequent changes in the SSTDR reflection response. These changes in reflection response can be detected only if the changes are outside the window of uncertainty of the SSTDR measurement. In this paper, we establish a method of determining this window of uncertainty and the associated minimum-detectable change in impedance for SSTDR measurements. We demonstrate this for a photovoltaic (PV) systems, although the methods could be similarly applied to other applications. We assess the variability in SSTDR measurements caused by changes in the PV system that are representative of normal maintenance actions such as disconnecting/reconnecting a connector and completely breaking-down/setting-up the entire system. We evaluate how this variability translates to a minimum-detectable change in impedance and how that relates to common faults in PV systems (arc and ground faults, shading, damaged cells, and aging). We also describe methods of increasing SSTDR fidelity to accurately extract minor changes in impedance and therefore, detect small-magnitude electrical faults.

Monitoring the Effective Density of Airborne Nanoparticles in Real Time Using a Microfluidic Nanoparticle Analysis Chip.

Determining the effective density of airborne nanoparticles (NPs; particles smaller than 100 nm in diameter) at a point of interest is essential for toxicology and environmental studies, but it currently requires complex analysis systems comprising several high-precision instruments as well as a specially trained operator. To address these limitations, a field-portable and cost-efficient microfluidic NP analysis device is presented, which provides quantitative information on the effective density and size distribution of NPs in real time. Unlike conventional analysis systems, the device can operate in a standalone mode because of the chip operating principle based on the electrostatic/inertial classification and electrical detection methods. Moreover, the device is both compact (16.0 × 10.9 × 8.6 cm3) and light (950 g) owing to the hardware strip down enabled by integrating the essential functions for effective density analysis on a single chip. Quantitative experiments performed to simulate real-life applications utilizing effective density (i.e., effective density-based morphology analysis on engineered NPs and multi-parametric NP monitoring in ambient air) demonstrate that the developed device can be used as an analysis tool in toxicological studies as an on-site sensor for the monitoring of individual NP exposure and environments, for quality monitoring of engineered NPs via aerosol synthesis, and other applications.

Microfluidic ultrafine particle dosimeter using an electrical detection method with a machine-learning-aided algorithm for real-time monitoring of particle density and size distribution

Growing concerns related to the adverse health effects of airborne ultrafine particles (UFPs; particles smaller than 300 nm) have highlighted the need for field-portable, cost-efficient, real-time UFP dosimeters to monitor individual exposure. These dosimeters must measure both the particle density and size distribution as these parameters are essential to the determination of where and how many UFPs will be deposited in human lungs. However, though various kinds of laboratory-grade instruments and hand-held monitors have been developed, they are expensive and only capable of measuring particle size distribution. A microfluidic UFP dosimeter is proposed in this study to address these limitations. The proposed sensor, based on an electrical detection method with a machine-learning-aided algorithm, can simultaneously measure the size distribution (number concentration, mean mobility diameter, geometric standard deviation) and particle density, and is compact owing to the microelectromechanical systems (MEMS) technology. In a comparison test using physically synthesised Ag and di-ethyl-hexyl sebacate (DEHS) aerosols, the mean measurement errors of the proposed sensor compared to the reference system were 6.1%, 4.5%, and 7.3% for number concentration, mean mobility diameter, and particle density, respectively. Moreover, when the machine-learning aided algorithm was operated, the geometric standard deviation could be deduced with a 7.6% difference. These results indicate that the proposed device can be successfully used as a field-portable UFP sensor to assess individual exposure, an on-site monitor for ambient air pollution, an analysis tool in toxicological studies of inhaled particles, for quality assurance of nanomaterials engineered via aerosol synthesis, etc.

Partial Discharge Location Identification Using Permutation Entropy Based Instantaneous Energy Features

Localization of partial discharge (PD) is a reliable and necessary technique for early prediction of impending failure in transformer windings. Different PD sources have diverse impacts on insulation, and PDs measured at different locations exhibit unique energy characteristics. An approach based on the energy characteristics of the PD signal is proposed here to locate PD inside the winding. An electrical detection method has been considered with PD pulses measured at the top and bottom of the winding through high-frequency current transformers (HFCTs). To reveal multiscale intrinsic characteristics of the PD signal, authors extracted intrinsic mode function (IMF) using ensemble empirical mode decomposition (EEMD). Next, the instantaneous energy distribution-permutation entropy (PE-IED) values of the first several IMFs were calculated to capture the energy of the PD. Then, dimensionality reduction was ensured by the fast principal component analysis that uses the fixed-point algorithm method. Finally, adaptive density-based clustering using the nearest neighbor graph (ADBSCAN-NNG) approach is proposed to identify the local high-density energy components. The proposed method is verified using the winding of a vacuum cast coil transformer and the localization results reveal the feasibility of the proposed method in locating PDs over existing localization algorithms. The reduced complexity of the proposed algorithm ensures the accurate onsite PD localization in transformer windings.

Pore-forming toxins as tools for polymer analytics: From sizing to sequencing.

We report here on the nanopore resistive pulse sensing (Np-RPS) method, involving pore-forming toxins as tools for polymer analytics at single molecule level. Np-RPS is an electrical method for the label-free detection of single molecules. A molecule interacting with the pore causes a change of the electrical resistance of the pore, called a resistive pulse, associated with a measurable transient current blockade. The features of the blockades, in particular their depth and duration, contain information on the molecular properties of the analyte. We first revisit the history of Np-RPS, then we discuss the effect of the configuration of the molecule/nanopore interaction on the molecular information that can be extracted from the signal, illustrated in two different regimes that either favor molecular sequencing or molecular sizing. Specifically, we focus on the sizing regime and on the use of two different pore-forming toxins, staphylococcal α-hemolysin (αHL) and aerolysin (AeL) nanopores, for the characterization of water-soluble polymers (poly-(ethylene glycol), (PEG)), homopeptides, and heteropeptides. We discuss how nanopore sizing of polymers could be envisioned as a new approach for peptide/protein sequencing.

Fault prevention and application of electrical automation equipment based on maintenance methods

Electrical automation equipment can perform unified processing, research and development control of relevant information with the support of computers, effectively improving the safety and stability of equipment operation. However, electrical machinery equipment systems are susceptible to interference from a variety of factors during operation, and are prone to failures, which will seriously affect normal production. Based on this, this article conducts in-depth research on electrical automation equipment faults, and puts forward the main strategies of electrical automation equipment fault prevention and the maintenance methods of common faults. Aiming at the problem that the number of correct detection fault parameters in the traditional electrical automation equipment detection method is small and the accuracy of the detection result is affected, research on the field fault detection method of electrical automation equipment is carried out. The on-site fault information collection of electrical automation equipment, fault information fusion combined with information link technology, and on-site fault diagnosis methods of electrical automation equipment ensure the accuracy requirements of industrial production enterprises for fault detection results.

Fast Detection Using PID Pre-Interpolation Algorithm for Magnetic Particle Testing

The current tracing scheme is based on a silicon-controlled rectifier (SCR) with phase shift triggering, which is utilized in traditional magnetic particle testing. In industrial applications, the popularization and implementation of magnetic particle flaw detection technology could be degraded by various problems such as poor real-time performance of current tracing, the significant error of current tracing, and low precision of flaw detection. In this work, the magnetization current waveforms under different control angles of the SCR are studied to obtain a real-time magnetization current tracking in less than 0.5 s and a current tracking error smaller than 5%. A fast three-phase electric zero detection circuit is designed in which the current tracking method is optimized by combining a high-speed analog to digital (AD) converter with a true root-mean-square (true-rms) device. Moreover, a Newton interpolation-based fast proportional integral derivative (PID) algorithm is proposed to achieve a precise real-time current tracking. The experimental results show that the real-time magnetic particle flaw detection for current tracking is performed in less than 0.2 s with a current tracking error below 2%, which meets the technical requirements of magnetic particle flaw detection. The fast three-phase electric zero detection method using the PID algorithm can improve the real-time performance and accuracy of current tracking in magnetic particle flaw detection.

RETRACTED ARTICLE: Rigorous reduction of partial shading condition in grid connected solar PV system using discrete time-based PSO controller

Solar industry has seen tremendous development over the past decade and in particular, the PV (Photovoltaic) system which has an imperative advancement almost in all fields of science. Envisaging various faults in the PV system will significantly enhance the efficiency, reliability, and life of the PV system. The main vulnerable element in the PV system which is subjected under different weather condition causes total damage to the system. Proper monitoring and maintenance are needed to increase the life of the system. Several investigations were made so far to predict the faults in the PV system such as the Visual method, the Thermal method, the Electrical detection method, the Machine learning techniques, the Arc fault detection technique and the Protection device-based techniques were used generally. But more effective fault diagnosis techniques are required for PV arrays. The proposes a novel method for reducing the partial shading condition in solar PV system connected to a grid which consists of a discrete time-based particle swarm optimization (PSO) controller that controls the irradiation or partial shading as well as any short circuits in PV cell. Hence, this proposed work enhances with producing efficient energy by achieving high predictive accuracy of about 99%, high efficiency of about 98.9% and low THD (0.9) under partial shading conditions as well as harmonics.