Electrical Failure Research Articles

О способах определения износа коммутационных аппаратов электроподвижного состава

The article provides the statistics analysis of electric locomotives 2ES6 failures. The factors influencing the wear of the switching equipment of the electric rolling stock are determined. Methods of forecasting the wear of switching equipment based on the model of changes in the transient contact resistance during operation are considered.

ВЛИЯНИЕ РЕЗОНАНСНЫХ ЯВЛЕНИЙ НА ТЕХНИЧЕСКИЙ РЕСУРС АСИНХРОННЫХ ЭЛЕКТРОДВИГАТЕЛЕЙ ПРИ ИМПУЛЬСНЫХ ПЕРЕНАПРЯЖЕНИЯХ

The relevance The work is devoted to assessing the impulse overvoltages effect at the input of the supply cable on the asynchronous machines technical resource as a solution to the electrical equipment failure problem caused by the resource loss. Aim of research Reducing the asynchronous machines accident rate by monitoring their technical condition and the implementation of electrical equipment riskbased maintenance during impulse overvoltages. Research methods The asynchronous electric motor, a supply cable, lightning effects and surge arrestor simulation modeling was used; Fourier analysis was applied. Results The simulation model of electric motor supply system section with installed surge arrestors in transformer substation cells was developed. Spectral composition of input voltage and current of asynchronous machine depending on cable line length was obtained. Assessment of technical resource of asynchronous machine based on voltages and currents oscillograms on its input and the related value of energy transmitted to its insulation during impulse overvoltages was realized.

Highly Robust Flexible Poly-Si Thin Film Transistor Under Mechanical Strain With Split Active Layer for Foldable Active Matrix Organic Light Emitting Diode Display

We report a novel active split structure of low-temperature polysilicon (LTPS) thin film transistor (TFT) on polyimide (PI) substrate showing robust electrical performance under mechanical strain. The compressive and tensile bending tests were carried out with cylinders of radii 3, 2 and 1 mm respectively. The active split TFT exhibits much more stable electrical performance than the conventional TFT. The conventional LTPS TFT shows electrical failure due to the crack generation during tensile or compressive bending test carried out with 1 mm radius cylinder. The split poly-Si islands could resist crack formation/propagation, resulting in a robust electrical performance.

생애이력 기반 특고압 전기설비 안전관리 서비스를 위한 시스템 기술 개발

Accidents occurring in high-voltage electrical equipment cause human injury, so the demand for accident prevention and maintenance of electrical equipment is increasing. ICT technology has been introduced to safety management services, which have been mainly managed based on human, and an electrical safety management system is developing to prevent electrical disasters and electrical equipment failures. The personal electric equipment is defined as a power distribution system that receives the electricity with a voltage of 1000 V or more or a receiving capacity of 75kW or more from a utility and uses the electricity at the receiving location such as a building or a factory. This paper describes the implementation of a system to provide safety management services of electrical equipment based on the life cycle, including the installation, inspection, safety management, and disposal of high voltage electrical installations. And it shows the demonstration site to which the development system is applied.

Fault monitoring and diagnosis of motorized spindle in five-axis Machining Center based on CNN-SVM-PSO

A spindle fault diagnosis method based on CNN-SVM optimized by particle swarm algorithm (PSO) is proposed to address the problems of high failure rate of electric spindles of high precision CNC machine tools, while manual fault diagnosis is a tedious task and low efficiency. The model uses a convolutional neural network (CNN) model as a deep feature miner and a support vector machine (SVM) as a fault state classifier. Taking the electric spindle of a five-axis machining centre as the experimental research object, the model classifies and predicts four labelled states: normal state of the electric spindle, loose state of the rotating shaft and coupling, eccentric state of the motor air gap and damaged state of the bearing and rolling body, while introducing a particle swarm algorithm ( PSO) is introduced to optimize the hyperparameters in the model to improve the prediction effect. The results show that the proposed hybrid PSO-CNN-SVM model is able to monitor and diagnose the electric spindle failure of a 5-axis machining centre with an accuracy of 99.33%. In comparison with the BP model, SVM model, CNN model and CNN-SVM model, the accuracy of the model increased by 10%, 6%, 4% and 2% respectively, which shows that the fault diagnosis model proposed in the paper can monitor the operation status of the electric spindle more effectively and diagnose the type of electric spindle fault, so as to improve the maintenance strategy.

A Novel Method for Detection and Location of Series Arc Fault for Non-Intrusive Load Monitoring

Series arc faults cause the majority of household fires involving electrical failures or malfunctions. Low-fault current amplitude is the reason for the difficulties faced in implementing effective arc detection systems. The paper presents a novel arc detection and faulty line identification method. It can be easily used in the low-voltage Alternate Current (AC) household network for arc detection in the Non-Intrusive Load Monitoring (NILM). Unlike existing methods, the proposed approach exploits both current and voltage signal time domain analysis. Experiments have been conducted with up to six devices operating simultaneously in the same circuit with an arc fault generator based on the IEC 62606:2013 standard. Sixteen time-domain features were used to maximize the arc-fault detection accuracy for particular appliances. Performance of the random forest classifier for arc fault detection was evaluated for 28 sets of features with five different sampling rates. For the single period analysis arc, detection accuracy was 98.38%, with F-score of 0.9870, while in terms of the IEC 62606:2013 standard, it was 99.07%, with F-score of 0.9925. Location of a series arc fault (line selection) was realized by identifying devices powered by the faulty line. The line selection was based on the Mean Values of Changes feature vector (MVC50), calculated for absolute values of differences between adjacent current signal periods during the arc fault. The fault location accuracy was 93.20% for all cases and 98.20% for cases where the arc fault affected a single device.

Influence of impulse corona discharge on discharge time lag of a surge arrester with a spark gap

ZnO surge arrester with a spark gap is used for reducing electrical failures in power distribution systems of Japan. The high responsiveness, the low residual voltage, and the stable operating voltage are required for the arresters. However, the discharge time lag generally occurs in a spark gap. In this study, measurements of V-t characteristics and observations of optical emissions from impulse corona discharges generated at a newly designed small step edge on an inclined plane of each electrode were carried out. The small step edge made the discharge time lag and the standard deviation small. Based on the results, we indicated that the impulse corona discharge improves the discharge time lag of the sparkover.

Computational study of the Transmission pole during downburst using ANSYS, considering fluid-structure interactions

A downburst is described as a strong downdraft that causes a devastating wind outburst on or near the earth surface. Such occurrences are more common during thunderstorms. Downbursts generally impinge on the ground and convect radially in all directions from the point of contact. Downbursts have been widely documented as a regular cause of high voltage electrical transmission tower and pole failures in numerous nations. The flow dynamics of impinging jets are investigated using numerical models, with applicability to downburst-related high intensity winds. The downburst is simulated using three primary viscous models (, , and RANS). These three viscous models are used to simulate downburst in two dimensions. The RANS viscous model is used for further investigation. To determine the temporal variant characteristics of a downburst, both steady and transient state simulations are performed. The flow is quasi-periodic, with vortex rings formed by the initial jet instability impinging on the surface, where they find an unstable separation reattachment of the boundary layer. Fluid-structure interactions (FSI) are multiphysics problems that cannot be handled using single physics equations. To determine the impacts of downburst on transmission poles, a 2-D steel pole 8 metres high with a fixed base is put near the downburst. Downburst pressure is measured on a transmission pole. The stresses and deflections caused by the pressure distribution on the tower due to varying fluid domain dimensions and jet velocities on the transmission poles are determined using one way coupling, from 2-D transmission pole geometry to 3-D transmission pole geometry.

Electrical Machine Breakdown Monitoring System Using IoT

Nowadays, electrical machines of different types and designs are used in hybrid production systems. Electrical machines are needed to control and implement various mechanisms to produce final products. However, sometimes, electrical machines stop working and cause downtime. This affects workers’ productivity during peak hours and reduces business profits. To overcome this problem, a suitable machine-monitoring system is required so that the problem can be addressed and rectified immediately. In this paper, an IoT prototype for an electrical machine failure monitoring system using Heltec LoRa ESP32 as the microcontroller to communicate with the Blynk IoT platform server was proposed and developed. The monitored parameter values are voltage, current and motion sensor readings. A ZMPT101b voltage sensor is used to measure the AC voltage, while an ACS712 current sensor is used to measure the AC current flowing through the machine. In addition, a tilt sensor is used to detect the vibrating motion of the motor. The measured values indicate machine health and are monitored using the developed Blynk IoT GUI platform, which is accessible from mobile phones and the web. A notification from Blynk application will be sent to users to alert them about a specific condition when a value measured and uploaded to the Blynk cloud exceeds the configured average. Three tests were performed using the serial monitor, the Blynk application and the multimeter, and the results showed that the lowest percentage of error between the voltage sensor readings extracted from the serial monitor and the multimeter was 1.19% and the lowest percentage of error between the current sensor readings extracted from the serial monitor and the multimeter was 17.63%. Overall, all parameter values were successfully displayed on the IoT platform. In future works, the accuracy of the current sensor can be improved by integrating a signal conditioning circuit to filter out noise.

Wind and Seismic Response Control of Dynamically Similar Adjacent Buildings Connected Using Magneto-Rheological Dampers

Wind and/or earthquake-imposed loadings on two dynamically similar adjacent buildings cause vigorous shaking that can be mitigated using energy dissipating devices. Here, the vibration response control in such adjacent structures interconnected with semi-active magneto-rheological (MR) dampers is studied, which could also be used as a retrofitting measure in existing structures apart from employing them in new constructions. The semi-active nature of the MR damper is modeled using the popular Lyapunov control algorithm owing to its least computational efforts among the other considered control algorithms. The semi-active performance of the MR damper is compared with its two passive states, e.g., passive-off and passive-on, in which voltage applied to the damper is kept constant throughout the occurrence of a hazard, to establish its effectiveness even during the probable electric power failure during the wind or seismic hazards. The performance of the MR damper, in terms of structural response reduction, is compared with other popular energy dissipating devices, such as viscous and friction dampers. Four damper arrangements have been considered to arrive at the most effective configuration for interconnecting the two adjoining structures. Structural responses are recorded in terms of storey displacement, storey acceleration, and storey shear forces. Coupling the two adjacent dynamically similar buildings results in over a 50% reduction in the structural vibration against both wind and earthquake hazards, and this is achieved by not necessarily connecting all the floors of the structures with dampers. The comparative analysis indicates that the semi-active MR damper is more effective for response control than the other passive dampers.

SOON: Social Network of Machines Solution for Predictive Maintenance of Electrical Drive in Industry 4.0

Abstract Predictive methods represent techniques commonly met in Industry 4.0 that offer a way to early predict or detect faults of machines, devices or tools. This is useful to anticipate failures with the main goal of improving maintenance planning. Making such predictions could decrease the unexpected malfunction operation or manufacturing downtime and consequently the overall maintenance costs. In this paper we present the basis of the architecture designed for predictive maintenance in the project Social Network of Machines (SOON) under the paradigm of Industry 4.0, as well as a brief literature state-of-the-art survey of the topic. A particular implementation of this architecture, a testbed for electrical motors failure detection, is shown and evaluated.

A robust principal component analysis-based approach for detection of a stator inter-turn fault in induction motors

Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries. A stator inter-turn fault (SITF) is considered to be the most common electrical failure according to statistical studies. In this paper, an algorithm for the detection of an SITF is presented. It is based on one of the blind source separation techniques called principal component analysis (PCA). The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components. The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion. The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply. In addition, with a straightforward and low computational burden in the fault detection process, the proposed method is computationally efficient. To evaluate the performance of the proposed method, large numbers of practical and simulation scenarios are considered, and the results confirm the good performance, high degree of accuracy, and good convergence speed of the proposed method.

New Selective Directional Ground Fault Protection Method for Ungrounded Systems

Ground faults are the main cause of electrical failure in transmission and distribution systems. In ungrounded distribution networks, it is difficult to detect where a first ground fault has happened as only capacitive currents are available. Also, it is important to trip only the faulty line in order to ensure the system's reliability. Directional residual overcurrent relays are the standard solution for these systems, but their difficult commissioning and high costs make it not trivial. This paper introduces a new selective and directional protection method to determine the faulty line in ungrounded medium voltage networks with three or more feeders. The method evaluates the residual currents in the feeders coupled to the same busbars in combination with a reference current created by the sum of a combination of residual currents of all feeders. Both simulation and laboratory results validate this new method, which is easy to start up, as it does not need previous configurations for its implementation, and it presents an economical solution to this problem.

Design and Parametric Analysis of a Wide-Angle and Polarization Insensitive Ultra-Broadband Metamaterial Absorber for Visible Optical Wavelength Applications

Researchers are trying to work out how to make a broadband response metamaterial absorber (MMA). Electromagnetic (EM) waves that can pass through the atmosphere and reach the ground are most commonly used in the visible frequency range. In addition, they are used to detect faults, inspect tapped live-powered components, electrical failures, and thermal leaking hot spots. This research provides a numerical analysis of a compact split ring resonator (SRR) and circular ring resonator (CRR) based metamaterial absorber (MMA) using a three-layer substrate material configuration for wideband visible optical wavelength applications. The proposed metamaterial absorber has an overall unit cell size of 800 nm × 800 nm × 175 nm in both TE and TM mode simulations and it achieved above 80% absorbance in the visible spectrums from 450 nm to 650 nm wavelength. The proposed MA performed a maximum absorptivity of 99.99% at 557 nm. In addition, the steady absorption property has a broad range of oblique incidence angle stability. The polarization conversion ratio (PCR) is evaluated to ensure that the MMA is perfect. Both TM and TE modes can observe polarization insensitivity and wide-angle incidence angle stability with 18° bending effects. Moreover, a structural study using electric and magnetic fields was carried out to better understand the MMA's absorption properties. The observable novelty of the proposed metamaterial is compact in size compared with reference paper, and it achieves an average absorbance of 91.82% for visible optical wavelength. The proposed MMA also has bendable properties. The proposed MMA validation has been done by two numerical simulation software. The MMA has diverse applications, such as color image, wide-angle stability, substantial absorption, absolute invisible layers, thermal imaging, and magnetic resonance imaging (MRI) applications.

Electric Flash Butt Rail Weld Failure Called “Concave–Convex”

Electric Flash Butt Rail Weld Failure Called “Concave–Convex”

ENSIGHTS: Intelligent Monitoring of Electric Power Transmission Assets

This work aims to use Data Science techniques to build predictive models that will eventually improve maintenance plans regarding power transformers by reducing shutoffs and transmission downtime. This work is part of a 36-month long Research and Development (R&D) project started on January 2021, as of the writing of this report the project is halfway through. The analytic models described herein have already been tested while most of the remaining work is yet to be done. In a single Data Lake environment, we will consolidate several databases from information systems that support the company's operation and maintenance processes. Various machine learning models will run on this data, and their results will appear in a dashboard, alongside several traditional indicators. This process will be integrated and consolidated in a computing platform with cloud architecture. We use Machine Learning (ML) to develop the models. Based on an Agnostic Probably Approximately Correct (PAC) Learning study of the available datasets estimating their Vapnik–Chervonenkis dimension, we choose Random Forests (RF) algorithms to be used in the new indicators. So far, the project has produced two new indicators: Chromatographic Assay Indicator (CAI) and Electrical Failure Risk Indicator (EFRI). The CAI indicator evaluation uses a Random Forest Algorithm trained with an external dataset due to the small number of power transformers failures in the O&M data. This indicator performed much better than classical chromatographic indicators to predict electric or temperature problems on the test set. The EFRI indicator correlates monitoring data available from an existing Supervisory Control and Data Acquisition (SCADA) system with maintenance data from an existing Enterprise Resource Planning (ERP) system through a RF algorithm capable of alerting to a higher risk of electric failure. ANEEL funds this work as R&D project PD-00394-1907/2019 titled “Aplicabilidade de nova tecnologia voltada para o desenvolvimento de um modelo de monitoramento inteligente dos ativos de transmissão”.

ENSIGHTS: Intelligent Monitoring of Electric Power Transmission Assets

This work aims to use Data Science techniques to build predictive models that will eventually improve maintenance plans regarding power transformers by reducing shutoffs and transmission downtime. This work is part of a 36-month long Research and Development (R&D) project started on January 2021, as of the writing of this report the project is halfway through. The analytic models described herein have already been tested while most of the remaining work is yet to be done. In a single Data Lake environment, we will consolidate several databases from information systems that support the company's operation and maintenance processes. Various machine learning models will run on this data, and their results will appear in a dashboard, alongside several traditional indicators. This process will be integrated and consolidated in a computing platform with cloud architecture. We use Machine Learning (ML) to develop the models. Based on an Agnostic Probably Approximately Correct (PAC) Learning study of the available datasets estimating their Vapnik–Chervonenkis dimension, we choose Random Forests (RF) algorithms to be used in the new indicators. So far, the project has produced two new indicators: Chromatographic Assay Indicator (CAI) and Electrical Failure Risk Indicator (EFRI). The CAI indicator evaluation uses a Random Forest Algorithm trained with an external dataset due to the small number of power transformers failures in the O&M data. This indicator performed much better than classical chromatographic indicators to predict electric or temperature problems on the test set. The EFRI indicator correlates monitoring data available from an existing Supervisory Control and Data Acquisition (SCADA) system with maintenance data from an existing Enterprise Resource Planning (ERP) system through a RF algorithm capable of alerting to a higher risk of electric failure. ANEEL funds this work as R&D project PD-00394-1907/2019 titled “Aplicabilidade de nova tecnologia voltada para o desenvolvimento de um modelo de monitoramento inteligente dos ativos de transmissão”.

A Data-Based Reliability Analysis of ESP Failures in Oil Production Wells

Electrical Submersible Pumps (ESPs) are one of the most widely used artificial lift methods in the petroleum industry. However, ESP failures are unanticipated and common occurrences with significant financial impacts for the operators. Analysis of the ESP performance and failures are essential in its design and optimization. This paper presents a statistical approach for diagnosing and evaluating the root causes of ESP failures. The analysis is based on the field data gathered from the surface and downhole ESP monitoring equipment over five years of production of 10 wells. Electrical failures are the most common general cause of ESP failures, accounting for 61% of all failures, followed by motor failure and gas locking. Specifically, power failure, under-voltage, voltage unbalance, and motor underload are the most common occurrences. The data trends are analyzed for the two weeks before each specific failure, and conclusions are made on the warning signs to predict failures. In addition, a Weibull statistical analysis model is constructed to evaluate the reliability features and estimate the ESP failure probability, allowing operators to perform preventive maintenance. The results provide guidelines for ESP operations and contribute to reducing or preventing ESP downtimes and operating costs.

Three‐Dimensional Transformation of Membrane‐Type Electronics Using Transient Microfluidic Channels for the Sequential Selective Plasticization of Supportive Plastic Substrates

Three‐Dimensional Transformation of Membrane‐Type Electronics Using Transient Microfluidic Channels for the Sequential Selective Plasticization of Supportive Plastic Substrates

Dynamic electrical failure of carbon nanotube ribbons

Emerging carbon nanotube (CNT)-based cutting-edge applications such as fast-response artificial muscle, fast-charging energy storage system, lightning strike protector, etc. Call for the study of dynamic electrical failure (DEF) of CNTs. In addition, the reported ampacities of CNTs were tested under different electrical signal applying conditions (e.g. the duration of testing), which led to the direct comparison of them may be virtually invalid. Here, the DEF behavior and mechanism of carbon nanotube (CNT) ribbons was studied by experiments and theoretical calculations as well. The considerable impact of electrical stress rates (ESRs) is demonstrated. Electrical failure resistance (EFR) of CNT ribbons increases with increasing ESR (from 0.5 V/s to 200 V/s), both in air and vacuum. EFR in vacuum is generally higher than that in air. This discrepancy attenuates significantly at high ESR regime due to the vacuumizing oxygen failure (VOF) mechanism (thermal oxidation is largely suppressed) in air. The DEF behavior is outlined by a theoretical model and a cube root relationship between EFR and ESR is obtained. We obtain that the way to apply electrical signal has a significant effect on the testing ampacity result and the establishment of a uniform standard of ampacity testing method is highly recommended.