Transient Current Signals Research Articles

Fault detection and classification in overhead transmission lines through comprehensive feature extraction using temporal convolution neural network

AbstractFaults in transmission lines cause instability of power system and result in degrading end users sophisticated equipment. Therefore, in case of fault and for the quick restoration of problematic phases, reliable and accurate fault detection and classification techniques are required to categorize the faults in a minimum time. In this work, 500 kV transmission line (Jamshoro‐New Karachi), Sindh, Pakistan has been modeled in MATLAB. The discrete wavelet transform (DWT) has been used to extract features from the transient current signal for different faults in 500 kV transmission line under various parameters such as fault location, fault inception angle, ground resistance and fault resistance and time series data has been obtained for fault classification. Moreover, the temporal convolutional neural network (TCN) is used for fault classification in 500 kV transmission network due to its robust framework. From simulation results, it is found that faults in 500 kV transmission line are classified with 99.9% accuracy. Furthermore, the simulation results of the TCN model compared to bidirectional long short‐term memory (BiLSTM) and Gated Recurrent Unit (GRU) and it has been found that TCN model is capable of classifying faults in 500 kV transmission line with high accuracy due to its ability to handle long receptive field size, less memory requirement and parallel processing due to dilated causal convolutions. Through this work, the meantime to repair of 500 kV transmission line can be reduced.

Nano-Impact Electrochemistry Reveals Kinetics Information of Metal-Ion Battery Materials with Multiple Redox Centers.

Prussian blue (PB) has emerged as a promising cathode material in aqueous batteries. It possesses two distinct redox centers, and the potassium ions (K+) are unevenly distributed throughout the compound, adding complexity to the interpretation of the K+ insertion/de-insertion kinetic mechanism. Traditional ensemble-averaged measurements are limited in uncovering the precise kinetic information of the PB particles, as the results are influenced by the construction of the porous composite electrode and the redox behavior from different particles. In this study, the electrochemical processes of individual PB particles were investigated using nano-impact electrochemistry. By varying the potentials, different types of transient current signals were obtained that revealed the kinetic mechanism of each oxidation/reduction reaction in combination with theoretical simulation. Additionally, a partially contradictory conclusion between single-particle analysis and the ensemble-averaged measurement was discussed. These findings contribute to a better understanding of the electrochemical processes of cathode materials with multiple redox centers, which facilitates the development of effective strategies to optimize these materials.

Nano‐Impact Electrochemistry Reveals Kinetics Information of Metal‐Ion Battery Materials with Multiple Redox Centers

AbstractPrussian blue (PB) has emerged as a promising cathode material in aqueous batteries. It possesses two distinct redox centers, and the potassium ions (K+) are unevenly distributed throughout the compound, adding complexity to the interpretation of the K+ insertion/de‐insertion kinetic mechanism. Traditional ensemble‐averaged measurements are limited in uncovering the precise kinetic information of the PB particles, as the results are influenced by the construction of the porous composite electrode and the redox behavior from different particles. In this study, the electrochemical processes of individual PB particles were investigated using nano‐impact electrochemistry. By varying the potentials, different types of transient current signals were obtained that revealed the kinetic mechanism of each oxidation/reduction reaction in combination with theoretical simulation. Additionally, a partially contradictory conclusion between single‐particle analysis and the ensemble‐averaged measurement was discussed. These findings contribute to a better understanding of the electrochemical processes of cathode materials with multiple redox centers, which facilitates the development of effective strategies to optimize these materials.

DC ground fault monitoring method of electrical equipment in 110 kV smart substation based on improved rough set

Abstract In order to improve the effect and timeliness of ground fault monitoring and improve the effect of fault early warning, a DC ground fault monitoring method for electrical equipment in 110 kV smart substations based on improved rough sets is proposed. The characteristics of the DC ground fault current are analyzed, and the equivalent model of the DC system is constructed according to the analysis results. The low-frequency signal is enhanced according to the fuzzy rough set, and the low-frequency sampled transient zero-sequence current signal is enhanced preprocessing and wavelet packet decomposition according to the signal enhancement proportional coefficient, and the fault line is selected according to the principle of maximum energy. The wavelet packet transform method is used to decompose and analyze the waveform of the leakage current generated when a single-point grounding fault occurs in a branch of the DC system, and the grounding fault location of the DC system of the substation is realized by relative entropy operation. With the help of the cloud platform, a DC ground fault monitoring platform is built, and the ground fault monitoring is completed by using this platform. The experimental results show that the method can obtain the characteristic harmonics of the ground fault, accurately monitor whether the mutation occurs, and can realize the early warning of the ground fault of the electrical equipment in a faster time, indicating that it can realize the accurate and timely monitoring of the ground fault.

Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems

Fast and accurate fault detection is important for the long term, stable operation of the distribution network. For the resonant grounding system, the fault signal features extraction difficulties, and the existing detection method’s accuracy is not high. A ground fault detection method based on fault data stitching and image generation of resonant grounding distribution systems is proposed. Firstly, considering the correlation between the transient zero-sequence current (TZSC) of faulty and healthy feeders under the same operating conditions, a fault data stitching method is proposed, which splices the transient zero-sequence current signals of each feeder into system fault data, and then converts the system fault data into grayscale images by combining the signal-to-image conversion method. Then, an improved convolutional neural network (CNN) is used to train the grayscale images and then implement fault detection. The simulation results show that the proposed method has high accuracy and strong robustness compared with existing fault detection methods.

Penetration of DWT & ANFIS to Power Transmission Disturbances

This study proposes a hybrid method to classify and estimate the location of short circuit disturbance on power transmission lines. The hybrid method uses Discrete Wavelet Transform (DWT) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The transmission system is implemented in a real system, in which the electric power transmission system on the KP bus to the GS bus is with a length of 64 Km. The DWT is used to process information from each phase voltage and current transient signal as well as the zero-sequence current for one cycle after the disturbance has started. The ANFIS classification is designed to detect disturbance on each phase and ground in determining the type of short circuit disturbance. ANFIS estimation is used to measure the location of disturbance that occur on the transmission line. The training and testing data are generated by simulating the types of short circuit disturbance using software with variations in disturbance location and fault resistance. The result is that the disturbance classification is with 100% accuracy and the estimated disturbance location is with the lowest error of 0.0006% and the highest error is 0.03%.

A multivariate Gaussian mixture model for anomaly detection in transient current signature of control element drive mechanism

The analysis of transient coil current signals for anomaly detection in a control element drive mechanism (CEDM) involves identifying signature mismatch based on comparison to normal signal profiles. This paper presents an anomaly detection model for optimizing the health monitoring framework for CEDMs. Our approach is a machine learning clustering algorithm based on the multivariate Gaussian Mixture Model (GMM), for the detection of incipient abnormal transient signatures in CEDM coils. The underlying considerations for the choice of a GMM-based clustering approach are that (1) the transient coil current profiles at normal operational behavior are very similar, and (2) only very few data are usually available that describe anomalous CEDM behavior. Since available data is heavily skewed toward normal patterns, this methodology allows us to establish the norm for detecting anomalous patterns based on a preset detection threshold. The model evaluation result shows a very low false-negative rate for anomaly detection.

Fault Location and Faulty Line Selection in Transmission Networks: Application of Improved Gated Recurrent Unit

Wide-area measurement systems (WAMS) are efficient tools to estimate the fault location in the large-scale power systems. In this study, a fast and accurate deep learning based structure is proposed to detect the faulty lines and locate the fault based on the measured data of WAMS including phasor measurement units. To this end, a modulated gated recurrent unit deep learning based network named improved gated recurrent neural network has been proposed. The designed network, which has a recurrent neural network based structure, benefits from one update gate that can capture sudden changes in transient voltage and current signals and also enhance computational efficiency. Furthermore, to enhance robustness of the designed network under noisy conditions, a new informative-based loss function is formulated. The proposed loss function utilizes generalized form of mutual information to enhance robustness of designed network in the presence of Gaussian and non-Gaussian noises. The numerical results on IEEE 68-bus system verifies the effectiveness and superiority of the proposed method considering several operational conditions and comparison by two combined shallow-based networks and one deep learning based networks.

Scanning Electrochemical Microscopy Featuring Transient Current Signals in Carbon Nanopipets with Dilute or No Redox Mediator.

Herein, we report a sensitive scanning electrochemical microscopy (SECM) method based on the high transient current signals in carbon nanopipets (CNPs) under step potential waveforms. Taking advantage of the transient peak current, the approach curve can be conducted with very dilute (1 μM) or even no redox mediator and fitted by the scanning ion conductance microscopy (SICM) theory. In addition, a trace amount of electroactive species generated at the substrate can also be directly revealed from the transient current at the CNP tips. With the established feedback and generation/collection methods, we present the constant-height topography and electroactivity imaging of the substrates with only 1 μM K4Fe(CN)6. The developed new SECM method would allow the usage of CNPs to achieve both high sensitivity and spatial resolution with dilute or no redox mediator and thus find great potential applications in biological and electrocatalytic studies.

Deep-Learning Based Fault Events Analysis in Power Systems

The identification of fault types and their locations is crucial for power system protection/operation when a fault occurs in the lines. In general, this involves a human-in-the-loop analysis to capture the transient voltage and current signals using a common format for transient data exchange for power systems (COMTRADE) file. Then, protection engineers can identify the fault types and the line locations after the incident. This paper proposes intelligent and novel methods of faulty line and location detection based on convolutional neural networks in the power system. The three-phase fault information contained in the COMTRADE file is converted to an image file and extracted adaptively by the proposed CNN, which is trained by a large number of images under various kinds of fault conditions and factors. A 500 kV power system is simulated to generate different types of electromagnetic fault transients. The test results show that the proposed CNN-based analyzer can classify the fault types and locations under various conditions and reduce the fault analysis efforts.

Seeing Is Not Believing: Filtering Effects on Random Nature in Electrochemical Measurements of Single-Entity Collision.

To clarify the discrete nature of electrochemistry, single-entity electrochemistry of collision (SEEC) utilizes a confinement space in a nanoscale local electric field at a microscale electrode interface for characterizing single freely diffusing entities. This promising method provides new insights at the single entity level. However, the precise measurement is challenging because of the short residence time and wide current fluctuations caused by the dynamic and stochastic motion of a single entity at the interface of the electrode. Moreover, the enormous noise in the electrochemical system would submerge these weak transient electrochemical signals. To increase the signal-to-noise ratio, the low-pass filter (LPF) is often used but at the cost of lower temporal resolution. Therefore, a deeper understanding of the filtering effects on the electrochemical signal is required in SEEC. Here, we build a random walk model to simulate the dynamic electrochemical oxidation of individual silver nanoparticles (AgNPs) in the local electric field near the electrode. This model considers the effect of the effective potential during the interaction between NP and electrode. Results reveal that the shape of the signal is seriously distorted as the cutoff frequency (f c) of LPF is set at <20 kHz. Due to the filtering effects, hundreds of subpeaks originating from the dynamic motion of NP are merged in a simple peak, which muddies our "believing" from the "seeing" signals. However, the entire interaction time of single NPs with the electrodes can be acquired at f c ≥ 10 kHz. Moreover, an integral charge of the signal is conserved at any LPF, which enables quantitative analysis of SEEC. Our understanding of the filtering effect on single AgNPs oxidation is generally applicable to nano-electrochemical techniques (e.g., nanopore electrochemistry and nanopipette sensing) that generate transient current signals.

Multiple Fault Detection in Induction Motors through Homogeneity and Kurtosis Computation

In the last few years, induction motor fault detection has provoked great interest among researchers because it is a fundamental element of the electric-power industry, manufacturing enterprise, and services. Hence, considerable efforts have been carried out on developing reliable, low-cost procedures for fault diagnosis in induction motors (IM) since the early detection of any failure may prevent the machine from suffering a catastrophic damage. Therefore, many methodologies based on the IM startup transient current analysis have been proposed whose major disadvantages are the high mathematical complexity and demanding computational cost for their development. In this study, a straightforward procedure was introduced for identifying and classifying faults in IM. The proposed approach is based on the analysis of the startup transient current signal through the current signal homogeneity and the fourth central moment (kurtosis) analysis. These features are used for training a feed-forward, backpropagation artificial neural network used as a classifier. From experimentally obtained results, it was demonstrated that the brought-in scheme attained high certainty in recognizing and discriminating among five induction motor conditions, i.e., a motor in good physical condition (HLT), a motor with one broken rotor bar (1BRB), a motor with two broken rotor bars (2BRB), a motor with damage on the bearing outer race (BRN), and a motor with an unbalanced mechanical load (UNB).

Copper deposition on metallic and non‐metallic single particles via impact electrochemistry

This study demonstrates the possibility of depositing metals onto low-metal content particles via impact electrochemistry, a technique used to measure transient current signals (electrochemical impacts) produced from the collision between particles moving under Brownian motion and a potentiostated interface (Rees, 2014; Markham et al., 2020; Zhang and Zhou, 2020). The deposition of copper onto the surface of fly-ash cenospheres via electrochemical impacts is reported, along with its deposition onto silver and gold nanoparticles. A comparison with linear sweep voltammetry confirmed that impact signals correlated with deposition potentials (bulk and underpotential deposition). Reductive impact events were observed at potentials negative of −0.3 V (for Ag) and −0.1 V (for Au) (vs. MSE), with evidence for a change in coverage of deposition from ca. 103% at −0.1 V to 261% at −0.8 V vs. MSE for Au. Cenospheres were shown to be sufficiently electrochemically active to facilitate copper deposition, either on modified electrodes or showing transient impact spikes indicating copper deposition, which was confirmed via SEM/EDX and ICP-MS analysis.

High-Frequency Current Transformer Design and Construction Guide

High-frequency current transformers (HFCT) are well suited as sensors for measuring transient current signals on power cables, such as partial discharges (PD). If the HFCT is well designed and its measurement bandwidth matches the bandwidth of the signal to be measured, high sensitivities can be achieved. However, optimizing the HFCT design for a specific measurement bandwidth is a difficult task because various design parameters affect its transfer function. In addition, there is little helpful literature on the relationships between HFCT design and resulting sensitivity/bandwidth. To give guidance and fill this research gap, this article aims to provide all the necessary information on HFCT development. For this purpose, an analytical HFCT model is derived and validated with measurements from various self-made HFCT sensors. The method for measuring their transfer function (or transfer impedance) is presented and the measurement data of all our manufactured prototypes are given. Based on these data, valuable relationships between various design parameters and the HFCT transfer function are analyzed. Based on our experience, detailed information about the sensor manufacturing process is provided. The developed HFCT model is an effective tool to simulate and optimize the sensor transfer function before it is built. In this way, HFCT sensors can be designed quickly and cost-effectively specifically as required.

Radiation Hardness Property of Ultra-Fast 3D-Trench Electrode Silicon Detector on N-Type Substrate.

The radiation fluence of high luminosity LHC (HL-LHC) is predicted up to 1 × 1016 1 MeV neq/cm2 in the ATLAS and CMS experiments for the pixel detectors at the innermost layers. The increased radiation leads to the degradation of the detector properties, such as increased leakage current and full depletion voltage, and reduced signals and charge collection efficiency, which means it is necessary to develop the radiation hard semiconductor devices for very high luminosity colliders. In our previous study about ultra-fast 3D-trench electrode silicon detectors, through induced transient current simulation with different minimum ionizing particle (MIP) hitting positions, the ultra-fast response times ranging from 30 ps to 140 ps were verified. In this work, the full depletion voltage, breakdown voltage, leakage current, capacitance, weighting field and MIP induced transient current (signal) of the detector after radiation at different fluences will be simulated and calculated with professional software, namely the finite-element Technology Computer-Aided Design (TCAD) software frameworks. From analysis of the simulation results, one can predict the performance of the detector in heavy radiation environment. The fabrication of pixel detectors will be carried out in CMOS process platform of IMECAS based on ultra-pure high resistivity (up to 104 ohm·cm) silicon material.

Electrochemical collision of single graphene oxide sheets at ultramicroelectrodes and its usage as substrate for Pt nanoparticle deposition

AbstractRevealing the physiochemical properties of individual graphene oxide (GO) sheets is highly desired for both fundamental research interests and practical applications, especially for their surface functionalization and modification toward hybrid composites with high performance. Herein, we report three different strategies including surface blockage, capacitive charging, and electrochemical reduction to detect and study single GO sheets using single‐entity electrochemical methods. The size, surface charge, dielectric, and electrochemical features of single GO sheets can then be revealed and quantified from the transient current signals. Moreover, the adsorbed GO can confine the electrode surface for metal deposition and produce single Pt nanoparticles, as indicated by the discrete reductive spikes shown in the deposition processes.

New Time-Frequency Transient Features for Nonintrusive Load Monitoring

A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of signal processing techniques to extract features from voltage and current signals. This paper presents a new time-frequency feature based on Stockwell transform. The extracted features aim to describe the shape of the current transient signal by applying an energy measure on the fundamental and the harmonic frequency voices. In order to validate the proposed methodology, classical machine learning tools are applied (k-NN and decision tree classifiers) on two existing datasets (Controlled On/Off Loads Library (COOLL) and Home Equipment Laboratory Dataset (HELD1)). The classification rates achieved are clearly higher than that for other related studies in the literature, with 99.52% and 96.92% classification rates for the COOLL and HELD1 datasets, respectively.

Power System Transient Current Sensor Based on Magnetoresistance Effect

Aiming at the problem that the mixed use of overhead lines and cables causes serious refraction and attenuation of the traveling wave signal in propagation, which makes the traditional traveling wave sensor unable to adapt to the weak signal identification and recording requirements. A wideband sensor for detecting transient current signal of power system is developed. The detection principle of the anisotropic magnetoresistive sensor is analyzed, and the tiny signal precision differential amplifier circuit is designed. Based on the actual situation of the power system, a corresponding test environment was built to conduct transient current transfer tests. The experimental results show that the sensor has a high linearity in the 0-5MHz bandwidth, and is suitable for the extraction of various transient signals, especially in the power system traveling wave protection has high application value.

Study on Collided Ag or Pt Single Nanoparticle’s Current Peak Shape Changing Depending on Applying Potential

Electrocatalytic amplification method was used for analyzing electrochemical properties about collided nanoparticles. Silver nanoparticles (Ag NPs) and platinum nanoparticles (Pt NPs)’s characteristics about hydrazine oxidation reaction were investigated. Those NPs have distinct electrocatalytic activation for hydrazine oxidation reaction. For detecting these NPs, gold (Au) ultramicroelectrode (UME) was used as working electrode which has appropriate catalytic activities comparing with both NPs. When applied potential was above 0 V, Pt NPs showed positive electrocatalytic characteristic, then collided Pt NPs produced amplified transient current signal. In contrast, Ag NPs produced decreased transient current signal when colliding onto Au UME applied above 0.25 V which potential range Ag NPs showed negative electrocatalytic characteristics. But the transient current signals by each collided NPs showed shape shifting as changing applied potential. When applying relatively low potential range, current signals produced by each NPs were staircase shape which means the collided NPs sustain stable state on Au UME after collision events. After changing high potential range, the current signals were shifting to blip or spike shape. As analyzing current peak shape changing phenomenon, electrocatalytic reaction mechanisms by each NPs could be understood by this method.

Improved Transient Data Compression Algorithm Based on Wavelet Spectral Quantization Models

In this work a dedicated encoding algorithm is presented for current and voltage transient signals digitalized from electrical networks. New models for dynamic bit allocation based on adaptive and fixed spectral envelope estimation models for transformed coefficients are proposed. The fixed profiles adjust the number of bits to be used by the wavelet transform decomposition level, which is used in the transformed vector quantization. The models proposed to estimate the adaptive spectral envelope have the transformed spectrum segmented by subbands or by decomposition levels of the wavelet transform. The dynamic bit allocation is implemented according to the signal spectral behavior. The quantized coefficient vector is encoded using an entropy coder, and it is asynchronously packaged in a compressed file. A parametric configuration for optimization algorithm is also proposed for the fixed spectral profile models in order to improve performance. Simulation results are presented using a signal data bank with a set of reported events of electric power networks. Performance comparisons with other work are also presented.