Electrical Faults Research Articles

Microstructural Study of Arc Beads in Aluminum Alloy Wires with an Overcurrent Fault.

To clarify the understanding and analysis of arc molten marks in electrical faults of aluminum alloy wires, this paper simulates overcurrent faults of aluminum alloy wires at currents of 128 A–224 A and uses thermogravimetry-differential scanning calorimetry (TG-DSC), optical microscope (OM), scanning electron microscope (SEM) and X-ray energy spectroscopy (EDS) to characterize the effects of current on the microstructure of arc beads. The results show that there are small and large amounts of Al-Si and Al-Fe binary phases in the metallographic structure of the aluminum alloy wires at the rated current, the grains are fine, and there are no significant grain boundaries. After an overcurrent fault occurs in the wires, a high-temperature arc causes the second phase in the aluminum alloy to disappear, a cellular dendritic metallographic structure appears, the grain boundaries become more well-defined, and composition segregation occurs at the grain boundaries. Using Image-Pro-Plus software to quantify the grain characteristics, the average grain size is found to gradually decrease as the current increases. In addition, by comparing and analyzing the characteristics of arc beads in aluminum wires and aluminum alloy wires under the same conditions, alloying elements are found to have a refining effect on the grain boundaries, and there are coarse precipitates at the grain boundaries in the aluminum wire arc beads.

Artificial neural network–based fault diagnosis for induction motors under similar, interpolated and extrapolated operating conditions

The diagnosis of mechanical and electrical faults of induction motors (IMs) has been performed using artificial neural networks (ANN) for similar, interpolated and extrapolated operating speeds. The current and vibration signals of faulty and healthy IMs measured from a Machinery Fault Simulator are used in this work. In total, ten different IM fault conditions have been considered: four mechanical faults (bearing fault, unbalanced rotor, misaligned rotor, and bowed rotor), five electrical faults (broken rotor bar, phase unbalanced fault with two severity levels, and stator winding fault with two severity levels), and one healthy motor condition. An ANN model is developed in which raw time domain data of faulty IMs are used and the fault diagnosis is then performed for the motor’s various operating conditions. Initially, diagnosis is performed to predict and classify the motor faults, for the same operating conditions for which we trained ANN. The diagnosis is then extended for interpolated and extrapolated speeds in order to accomplish the diagnosis when data are not available at all the required operating speeds. From the results, it is found that the present ANN-based diagnosis is effective in the same speed case for various operating conditions (seven speeds as well as three loads). In addition, the diagnosis is found to be satisfactory for all interpolated and extrapolated speed cases. It is also observed that the present IM fault diagnosis is better in the interpolation speed cases than the extrapolation speed cases.

Electrical System Design and Fault Analysis of Machine Tool Based on Automatic Control

Automatically controlled machine tools have been used extensively in the industrial field, and fault analysis methods have garnered increasing attention. This paper first describes the software and hardware design of a machine tool and then presents a fault analysis of the machine tool. The fault types of machine tools are analyzed. A signal is obtained from a vibration sensor, the characteristic value is extracted, and the fault is analyzed using a back-propagation neural network (BPNN). The experimental results show that the BPNN yields the best performance when the structure is 8-9-8, and its recognition rate is 97.22% for different types of faults. Meanwhile, the recognition rate of naive Bayes is only 76.73%, and that of a support vector machine is only 85.55%, which is significantly lower than that of the BPNN. The results show that the BPNN is effective in fault analysis and can be promoted and applied more extensively.

Fault detection of electric vehicle motor based on flux performance using FEM

This paper presents the early faults detection in electric vehicle motor based on flux performance examination in defective electrical machine using finite element methods (FEM). Depend on time step, the proposed technique has been designed and examine to produce efficient method under high accuracy and short time to detect the faults in Electric Vehicle motors. To decrease the probability and time of electric motor faults, the early detection of these faults will give enough time to prevent many problems during the motion. The different waveforms timing of motor torque in every situation associated with the waveforms of stator current provide spreading in the proposed method. The results show fast fault detections and a Novel technology was established to extort the fault of induction motor.

Application of electric meter in the smart distribution network

The purpose is to promote equipment automation in the smart distribution network and improve the electricity detection accuracy and reliability of power equipment. Here, electric meters in the smart distribution network are studied. Firstly, the current electricity detection methods are discussed. Secondly, the common electric meter fault mechanisms are summarized and analyzed. Finally, the GM (Grey Mole) (1,1) is optimized through ACO (Ant Colony Optimization) algorithm and is used for detection and investigation of the real-time electric meter monitoring system. Then, simulation experiments are conducted based on the user’s electricity consumption data. The results indicate that the load correction value optimized by the ACO algorithm is closer to the actual load than the predicted value, and the absolute residual value is greatly reduced compared with the predicted value. At the same time, the prediction trend is more similar. This shows that the ACO-optimized GM can effectively improve the accuracy of the electric meter monitoring system.

Fault detection of electric vehicle motor based on flux performance using FEM

This paper presents the early faults detection in electric vehicle motor based on flux performance examination in defective electrical machine using finite element methods (FEM). Depend on time step, the proposed technique has been designed and examine to produce efficient method under high accuracy and short time to detect the faults in Electric Vehicle motors. To decrease the probability and time of electric motor faults, the early detection of these faults will give enough time to prevent many problems during the motion. The different waveforms timing of motor torque in every situation associated with the waveforms of stator current provide spreading in the proposed method. The results show fast fault detections and a Novel technology was established to extort the fault of induction motor.

A Data-Driven Long Time-Series Electrical Line Trip Fault Prediction Method Using an Improved Stacked-Informer Network.

The monitoring of electrical equipment and power grid systems is very essential and important for power transmission and distribution. It has great significances for predicting faults based on monitoring a long sequence in advance, so as to ensure the safe operation of the power system. Many studies such as recurrent neural network (RNN) and long short-term memory (LSTM) network have shown an outstanding ability in increasing the prediction accuracy. However, there still exist some limitations preventing those methods from predicting long time-series sequences in real-world applications. To address these issues, a data-driven method using an improved stacked-Informer network is proposed, and it is used for electrical line trip faults sequence prediction in this paper. This method constructs a stacked-Informer network to extract underlying features of long sequence time-series data well, and combines the gradient centralized (GC) technology with the optimizer to replace the previously used Adam optimizer in the original Informer network. It has a superior generalization ability and faster training efficiency. Data sequences used for the experimental validation are collected from the wind and solar hybrid substation located in Zhangjiakou city, China. The experimental results and concrete analysis prove that the presented method can improve fault sequence prediction accuracy and achieve fast training in real scenarios.

Estimation of Frequency-Dependent Impedances in Power Grids by Deep LSTM Autoencoder and Random Forest

This paper proposes a deep-learning-based method for frequency-dependent grid impedance estimation. Through measurement of voltages and currents at a specific system bus, the estimate of the grid impedance was obtained by first extracting the sequences of the time-dependent features for the measured data using a long short-term memory autoencoder (LSTM-AE) followed by a random forest (RF) regression method to find the nonlinear map function between extracted features and the corresponding grid impedance for a wide range of frequencies. The method was trained via simulation by using time-series measurements (i.e., voltage and current) for different system parameters and verified through several case studies. The obtained results show that: (1) extracting the time-dependent features of the voltage/current data improves the performance of the RF regression method; (2) the RF regression method is robust and allows grid impedance estimation within 1.5 grid cycles; (3) the proposed method can effectively estimate the grid impedance both in steady state and in case of large transients like electrical faults.

Fault Ride Through in Grid Integrated Hybrid System Using FACTS Device and Electric Vehicle Charging Station

Adopting eco-friendly solutions is the need of the hour in order to downscale carbon emissions and the fast depletion of fossil fuels. Hybrid energy systems provide one such optimistic sustainable solution for power generation in a grid integrated system as well as for stand-alone applications. With grid integrated systems, there are many grid codes to be maintained such as voltage stability, frequency deviation and Fault Ride Through Capability (FRT). In a hybrid system, the propensity of the PV/Wind system to remain connected at the moment of short electric fault is identified as FRT. This paper elucidates the voltage compensation using an Electric Vehicle (EV) charging station or a Flexible AC Transmission System (FACTS) device depending on the intensity of fault that occurs at the Point of Common Coupling (PCC) in grid integrated hybrid systems. When a fault occurs at the PCC, depending on the intensity of the voltage sag either the EV charging station or a FACTS device, namely a Dynamic Voltage Restore (DVR), provides the voltage compensation. The voltage obtained from an EV charging station or DVR is conditioned using power converters and fed to the PCC to even out the discrepancy in the voltage that is effected due to the fault. Even though charges electric vehicles continuously, the EV charging station gives priority to supply voltage for compensation whenever a fault occurs at the grid. If the intensity of voltage sag due to fault is between 0.9 to 0.51 p.u, the EV charging station provides voltage compensation, and for voltage sag between 0.5 to 0.2 p.u, DVR takes over to provide voltage compensation for the continuous sustainability of the grid. The proposed system makes use of an existing source such as an EV charging station as a supplementary device to provide compensation, and also has a backup supplementary device DVR in case of any non-availability of the EV charging station. Thus, the voltage compensation in turn facilitates the parameters such as DC link voltage and the grid voltage to stay within the pertinent limits in the event of a fault at the grid. The system was simulated using MATLAB Simulink and the results were verified.

Discrete-time backstepping-state feedback approach for current control of LCL grid-tied converters

Voltage source converters connected to the grid through an LCL filter effectively attenuate the high frequency harmonics generated by the PWM modulation. However, due to the intrinsic resonant peak of this filter and due to the discretization process, the controller design is commonly a challenge and it may not guarantee robustness against abnormalities of the grid. In order to overcome these problems, this paper proposes a new discrete-time current control scheme applied to grid-tied converters based on a backstepping algorithm and state feedback controller in a multi-loop approach. The tracking of converter-side current for a given reference, which is generated by the outer loop, is performed by the backstepping algorithm. In addition, the controller gain design methodology is presented taking into account an extended model, that includes the delay resulted from the discrete-time implementation. An analysis of the gain choice effect on the system closed-loop eigenvalues is demonstrated, which allows the designer to select an appropriate set of controller gains. As outer loop, a state feedback method combined with a state-space resonant controller is used for the grid-side current tracking. In this structure, the harmonic compensation is easily preformed, in addition, the feedback gains are chosen using an optimum algorithm. The robustness of the multi-loop backstepping based control structure is improved in comparison with a full state feedback controller. Simulation and experimental results are obtained to demonstrate the effectiveness of the proposed control scheme under different test conditions, as reference changes, operating during electric faults and grid impedance variation.

Lithium-ion energy storage battery explosion incidents

Utility-scale lithium-ion energy storage batteries are being installed at an accelerating rate in many parts of the world. Some of these batteries have experienced troubling fires and explosions. There have been two types of explosions; flammable gas explosions due to gases generated in battery thermal runaways, and electrical arc explosions leading to structural failure of battery electrical enclosures. The thermal runaway gas explosion scenarios, which can be initiated by various electrical faults, can be either prompt ignitions soon after a large flammable gas mixture is formed, or delayed ignitions associated with late entry of air and/or loss of gaseous fire suppression agent. The electrical explosions have entailed inadequate electrical protection to prevent high energy arcs within electrical boxes vulnerable to arc induced high pressures and thermal loads. Estimates of both deflagration pressures and arc explosion pressures are described along with their incident implications.

Deep learning based optimum fault diagnosis of electrical and mechanical faults in induction motor

Among all the motors Induction motor (IM) plays a vital role in industry and the demand for their reliability and safe operation is increasing day by day. They are reliable but they do wear out if not maintained timely which in turn will lead to excessive loss of revenue and also man power and this motivates us to develop an intelligent methodology for diagnostic of incipient faults in Induction Motor. This paper focuses on the development of optimum Deep Learning based diagnostic technique to detect the mechanical and electrical faults in Induction Motor. Here, mechanical and electrical faults of different severity level are being tested on machine fault simulator using acquired current and vibrational signals. In this work, optimum model of Deep Neural Network (DNN) based on critical vibration and current features is developed and finally used to timely and effectively detect that which kind of faults the given IM is dealing with. The results are added and discussed in the result and discussions.

Research on thermal imaging fault detection system based on weibull distributed electrical system

A fault prediction analysis is performed In order to use infrared thermal imaging technology to detect electrical system faults, based on Weibull distribution technology. First, the principle is explained, the key technologies and factors of infrared thermal imaging and the electrical fault monitoring system are analyzed, and the available technologies are summarized. The detection process and analysis research are established with the help of on-site inspection and analysis results for power system fault analysis and research. The research shows that the method is effective for fault detection and trend analysis, and provides a reference for power fault detection and prediction analysis.

Two Graphical Shapes Based on DGA for Power Transformer Fault Types Discrimination

Different graphical diagnostic methods, which are based on dissolved gas analysis (DGA) are widely used for fault diagnosis and monitoring of dielectric liquid filled transformers. The aforementioned graphical diagnostic methods mainly focus on three, four, five or seven gases. In this work, an alternative approach is proposed by means of a two-shapes graphical method in order to discriminate between different fault types guided from the acetylene percentage derived from the DGA analysis. Low thermal faults are studied through the first shape (square), while electrical discharge faults are depicted in the second shape (pentagon). The accuracy of the proposed method is calculated based on 375 DGA data; the overall obtained accuracy is 78.93%.

Faults Diagnosis of BLDC Motors Using Artificial Neural Networks

Recently, brushless DC motor (BLDC) has been implemented in many applications, especially critical applications. Due to many reasons, BLDC motor subjects to many types of faults including electrical and mechanical faults, therefore detect and diagnosis faults is very important in order to keep safety to the motor also reduce cost and maintenance. In this paper, an approach has been presented to diagnose the stator winding faults, control circuit switches fault and bearing faults. Artificial Neural Network (ANN) has been applied to diagnose faults at different operation conditions. The simulation result shows the ability of the proposed technique to diagnose faults with high accuracy.

Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements

Frequency response analysis (FRA) is a well-known method to assess the mechanical integrity of the active parts of the power transformer. The measurement procedures of FRA are standardized as described in the IEEE and IEC standards. However, the interpretation of FRA results is far from reaching an accepted and definitive methodology as there is no reliable code available in the standard. As a contribution to this necessity, this paper presents an intelligent fault detection and classification algorithm using FRA results. The algorithm is based on a multilayer, feedforward, backpropagation artificial neural network (ANN). First, the adaptive frequency division algorithm is developed and various numerical indicators are used to quantify the differences between FRA traces and obtain feature sets for ANN. Finally, the classification model of ANN is developed to detect and classify different transformer conditions, i.e., healthy windings, healthy windings with saturated core, mechanical deformations, electrical faults, and reproducibility issues due to different test conditions. The database used in this study consists of FRA measurements from 80 power transformers of different designs, ratings, and different manufacturers. The results obtained give evidence of the effectiveness of the proposed classification model for power transformer fault diagnosis using FRA.

Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier

The main objective of the current work was to enhance the transformer fault diagnostic accuracy based on dissolved gas analysis (DGA) data with a proposed coupled system of support vector machine (SVM)-bat algorithm (BA) and Gaussian classifiers. Six electrical and thermal fault classes were categorized based on the IEC and IEEE standard rules. The concentration of five main combustible gases (hydrogen, methane, ethane, ethylene, and acetylene) was utilized as an input vector of the two classifiers. Two types of input vectors have been tested; the first input type considered the five gases in ppm, and the second input type considered the gases introduced in the percentage of the sum of the five gases. An extensive database of 481 had been used for training and testing phases (321 data samples for training and 160 data samples for testing). The SVM model conditioning parameter “λ” and penalty margin parameter “C” were adjusted through the bat algorithm to develop a maximum accuracy rate. The SVM-BA and Gaussian classifiers’ accuracy was evaluated and compared with several DGA techniques in the literature.

SF6 decomposed gas sensing performance of van der Waals layered cobalt oxyhydroxide: insights from a computational study.

The detection of SF6 decomposition products plays a significant part in identifying and assessing the electric discharge faults in SF6 insulation equipment. We performed dispersion corrected density functional theory calculations to study the adsorption performance of CoOOH upon SO2, SF4, SOF2, CF4, and SO2F2 toxic gases, to investigate their potential application as a gas sensor. The results clearly show a weak force between the CoOOH sheet, and the molecular gas with moderate adsorption strength enhances the desorption processes. According to Löwdin charge population analysis, electrons transfer from the molecular gas to the CoOOH surface, where the molecular gas behaves like an electron donor. The lower bandgap energy of the adsorption systems compared with pristine CoOOH significantly increases its electrical conductivity and gas sensing performance. The higher charge transfer and adsorption energy of the SOF2 adsorption system compared with the other four molecular gas is due to orbital hybridization around the Fermi energy. The theoretical computed adsorption energy with ultrahigh sensitivity and fast recovery time suggests that SF6 decomposed gases reusability is achieved with CoOOH as a resistance-type gas sensor.

Fault Detection Analysis for Three Phase Induction Motor Drive System using Neural Network

One of the most important components of the industrial process is known to be the three-phase induction motor. This device, however, is prone to electrical and mechanical faults, which may cause a substantial component or financial losses. The fault analysis received growing attention due to a need to increase reliability and to decrease potential output loss due to machine breakdown. Thus, the purpose of this paper is to present a simple and reliable fault analysis based on the Neural Network (NN) is proposed. The NN method is a simpler approach without a diagnostic professional to review data and diagnose issues. Various fault disputes of induction motor are developed and analysed using the NN method. The main types of faults considered are over-voltage, under-voltage, and unbalanced voltage faults. The trained network is tested with simulated fault current and voltage data.

A system-level dual-redundancy steer-by-wire system

Vehicle steer-by-wire system (SBW) can improve the handling stability, passive safety, active safety and drive comfort significantly, which becomes a hot topic in research. However, the safety problem of SBW is one of the most critical problem in its practical application. In order to solve the safety reliability problem of SBW, a dual-redundancy steer-by-wire system (DSBW) is proposed in this paper by applying the redundancy theory, in which no system failure is achieved when any single random electrical fault occurs, showing that DSBW is a system-level dual-redundancy system rather than a system with local redundancy. Finally, a DSBW prototype is manufactured to prove its high safety reliability. Fault injection experiments of the system are performed, in which DSBW works normally when any single electrical fault occurs.