Cable Fault Research Articles

Multiscale Analysis of the Aging Process of Cable Insulation

Cables, transmitting both power and signal, are essential in electric power systems. Due to the combined effect of electromagnetic and thermal fields, cable insulation aging starts from the destruction of polymer molecular structure and deteriorates in the form of insulation defects, resulting in the degradation of cables eventually. In this article, multiscale simulation is implemented to comprehensively analyze the insulation aging mechanism and reveal the variation law of aging characteristics. Regarding multiscales, by molecular dynamics (MD) simulation and finite-element method (FEM), the material and electrical parameters are calculated and quantitatively analyzed. According to the findings, tiny molecular products resulting from thermal and electrical aging are the main causes of dielectric constant and electrical conductivity variations. Field distribution inside the cable is distorted due to the degradation of material parameters, thus changing power loss and electrical parameters. The multiscale analysis method in this article can make the cable insulation aging analysis more time- and resource-efficient, providing the theoretical basis and data support for cable fault diagnosis and remaining useful life prediction.

The Cable Fault Diagnosis for XLPE Cable Based on 1DCNNs-BiLSTM Network

Diagnosing the fault type accurately from a variety of faults is very essential to ensure a stable electricity supply when a short-circuit fault occurs. In this paper, a hybrid classification model combining the one-dimensional convolutional neural network (1D-CNN) and the bidirectional long short-term memory network (BiLSTM) is proposed for the classification of cable short-circuit faults to improve the accuracy of fault diagnosis. Sample sets of the current signal for single-phase grounding short circuit, two-phase grounding short circuit, two-phase to phase short circuit, and three-phase grounding short-circuit are obtained by the simulink model, and the signal is input to this network model. The local features of the cable fault signals are extracted using 1D-CNN and the fault signal timing information is captured using BiLSTM, which enables the diagnosis of cable faults based on the automatically extracted features. The experimental results of the simulation show that the model can obtain a good recognition performance and can achieve an overall accuracy of 99.45% in classifying the four short-circuit faults with 500 iterations. In addition, the analysis of loss function curves and accuracy curves shows that the method performs better than networks with only temporal feature extraction, such as 1D-CNN and LSTM.

A novel fault monitoring method based on impedance estimation of power line communication equipment

Due to the close relationship between power grid fault and impedance, there is a significant defect in the use of power line communication (PLC) equipment to monitor and locate power grid faults, which is the lack of real-time impedance information. Therefore, this study proposes a new fault monitoring method based on impedance estimation of power line communication equipment. The channel frequency response (CFR) obtained from the PLC receiver is used to estimate the high-frequency input impedance to monitor and locate the ground fault of the power grid in real time. Firstly, based on the multi-conductor transmission line theory, bottom-up channel modeling method and impedance estimation technology, the basic principles of fault monitoring and location methods are clarified. Then, modeling and analysis of cables in different situations are carried out, and the characteristics of the factors affecting the impedance in CFR are extracted by variational modal decomposition (VMD), and the estimation results of impedance are obtained by inversion analysis. Based on this, the impedance change and machine learning algorithm are used to track and identify the abnormal state of the power line to achieve high-sensitivity positioning of the cable fault. The simulation results show that the method has a good identification effect on high and low impedance cable fault, and has a better effect on low resistance fault monitoring. The fault location error is less than 2.13%, and has a good positioning accuracy.

Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks

To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time-frequency domain joint impedance spectrum is proposed for cable fault identification and localization based on a deep belief network (DBN). Firstly, based on the distribution parameter model of power cables, we model and analyze the cables under normal operation and different fault types, and we obtain the headend input impedance spectrum and the headend input time-frequency domain impedance spectrum of cables under various operating conditions. The headend input impedance amplitude and phase of normal operation and different fault cables are extracted as the original input samples of the cable fault type identification model; the real part of the headend input time-frequency domain impedance of the fault cables is extracted as the original input samples of the cable fault location model. Then, the unsupervised pre-training and supervised inverse fine-tuning methods are used for automatically learning, training, and extracting the cable fault state features from the original input samples, and the DBN-based cable fault type recognition model and location model are constructed and used to realize the type recognition and location of cable faults. Finally, the proposed method is validated by simulation, and the results show that the method has good fault feature extraction capability and high fault type recognition and localization accuracy.

Step Frequency TR-MUSIC for Soft Fault Detection and Location in Coaxial Cable

Soft faults in cables may trigger short circuits and open circuits in time, in that, they ought to be detected and thus eliminated at an earliest possible stage, as to ensure safe and stable operation of the cables. A method called the time-reversal multiple signal classification (TR-MUSIC) had been proposed in the literature, which has been demonstrated to be an effective technique for locating soft faults in cables, owing to its high resolution and excellent noise robustness. However, traditional TR-MUSIC relies on a vector network analyzer for measuring the scattering matrix of cables, which adds cost and complexity to its implementation. In this regard, a new way of acquiring the desired scattering parameters is herein proposed. An arbitrary function generator is used to inject incident signals into the cable under test, and an oscilloscope is used to collect the reflected signals. After post-processing, the phase of scattering parameters can be obtained. There’s another key issue in the image of the detection results, ghost traces caused by the periodicity of Green’s function severely impact the vision saliency of the actual fault location, which limits the performance of the fault location. A step frequency variant of TR-MUSIC has been proposed, therefore, to mitigate ghost traces. Experimental results show that the fault location error of the proposed approach is smaller than 0.33% for a 51-m long coaxial cable. Moreover, in the case of introducing noise, the proposed approach can operate in situations with signal-to-noise ratios as low as –6 dB.

A Wavenumber Domain Reflectometry Approach to Locate and Image Line-Like Soft Faults in Cables

A Wavenumber Domain Reflectometry Approach to Locate and Image Line-Like Soft Faults in Cables

Tree Based Fault Classification in Underground Cable

This paper presents tree-based machine learning techniques like decision trees, and random forest algorithms to categorize various permanent faults such as a line to ground, line to line, line-line to ground, and line-line-line to ground fault simulated in an underground cable. The simulation has been performed with the variation in fault types, fault location, fault resistance, and fault inception angle and the sending end current signals are presented as input to the classifier for classifying the fault. A relative comparison between the decision tree technique and random forest technique has been performed and the random forest algorithm turns out to be the best for classifying the permanent faults in an underground cable. Both the algorithms are deliberated by precision, F1 score, and recall. The best result is presented by Random Forest Algorithm with an accuracy of 98.8%.

Research on Deep Learning-Based Detection of Cable Clamp Faults in Railway Tunnels

Research on Deep Learning-Based Detection of Cable Clamp Faults in Railway Tunnels

Concerning partial discharge detection in cable lines using high-frequency sensors

Partial discharges result in cable line faults that can occur in defective insulation systems made of rubber, polyethylene, XLPE, and flexible PVC. Insulation defects may occur due to disruptions in their production process, cable installation, and operational conditions. Therefore, the diagnostic and monitoring of partial discharge occurrence in cable insulation help prevent emergencies in damaged cable line sections. Partial discharge registration is a non-destructive diagnostic method that helps assess the cable line condition and localize the detected defects. There are various methods of operational monitoring and partial discharge cause analysis based on operative partial discharge sensors. The assessment of cable line condition depends on the efficiency of signal detection and processing by the sensors used. This article reviews the problems of detecting and localizing partial discharges in cable lines and junction boxes using HFCT and UHF sensors. Having analyzed the capabilities of these sensors, we believe that the combined usage of HFCT and UHF sensors is the best method of detecting and localizing partial discharge within the entire cable system (lines and junction boxes).

A Frequency Sweep Location Method for Soft Faults of Power Cables Based on MUSIC-Pseudospectrum

Due to the spurious peaks caused by multi-reflection and the randomness of the cable transfer function, the frequency domain reflection (FDR) method based on Fourier transform (FT) is inaccurate, even resulting in misjudging the soft faults of the cable. Thus, in this paper, a frequency sweep location method based on multiple signal classification (MUSIC) pseudospectrum is proposed to accurately diagnose and locate the soft faults. The idea of the method is to extend the cable transfer function into an array signal and estimate the MUSIC-pseudospectrum of the transfer function, which can transform the analysis of uncertain signals into the analysis of deterministic functions. Thus, the impact of the spurious peaks and the randomness of the cable transfer function can be eliminated. First, the transfer function of the cable is obtained by frequency sweep. Then the soft fault information in the transfer function is extracted and reconstructed by variational mode decomposition (VMD), which has a good ability to divide the harmonic frequency band. The MUSIC-pseudospectrum of the reconstructed signal is estimated to locate the cable soft faults. At last, the feasibility and effectiveness of the proposed method were verified by testing on a laboratory cable and field underground cable.

Power cable fired by transient arcing below the action value of relay protection: An analysis of a medium-voltage cable joint breakdown fault

Cable joint breakdown accidents have occurred frequently in recent years and have gradually become an important concern of the power maintenance department. The study of the fault development process of the cable joint is helpful to detect and diagnose in advance, so as to avoid the expansion of the accident. In this paper, a breakdown fault of 20 kV damp three-core cable joint was investigated. Firstly, the recorded fault waveform was analyzed, and the fault cable joint sample was disassembled. Then, the circuit simulation model was established to calculate the short-circuit resistance, which aimed to reveal the dynamic characteristics of the short-circuit resistance of the composite interface for the damp joint. Finally, the effect of moisture on the electric field distortion of the interface and the thermal ablation damage effect of the transient short-circuit current on the copper mesh were discussed by the finite element method. This paper reveals the phenomenon of transient grounding faults arising from moisture at the internal interface of cable joint, which may damage the grounding protection line of the joint and affect the sensitivity of the protective device. The method for analyzing the breakdown fault case of damp cable joint presented in this paper can provide reference and suggestions for the investigation and solution in similar cases.

Insight into gaseous product distribution of cross-linked polyethylene pyrolysis using ReaxFF MD simulation and TG-MS

Cross-linked polyethylene (XLPE) has been widely used as the insulation material of power cables due to the great electrical and thermal properties. However, the material deteriorated when subjected to excessive thermal stress, leading to performance reduction and even failure of the cables. In this paper, we proposed a novel thermal fault detection method in power cables by exploring the gaseous product distribution of XLPE pyrolysis. Firstly, the gas generation characteristics of the XLPE pyrolysis was investigated using large scale ReaxFF molecular dynamics simulation. The simulation results showed that the major gaseous products included H2, CH4, C2H6, C2H4, C2H2, CO. The pyrolysis kinetics as well as the formation mechanism of the gases was illustrated using the real-time trajectory obtained by molecular dynamic calculation. Furthermore, the influence of temperature on the gas product distribution was characterized. Higher temperature increased the yield of H2, C2H4, C2H2, but decreased that of CH4. The yield of C2H6 and CO almost kept constant as temperature changing. The influencing mechanism of temperature was reasonably explained by the proposed gas formation mechanism. On the basis of investigation on XLPE pyrolysis, a three ratios technique was proposed for the detection of thermal faults in power cables. Finally, the chemical composition of the gas product was validated by thermogravimetric and mass spectrometric simultaneous experiment. The experimental results exhibited good agreement with the numeric simulation.

Modern Approaches to Fault Location in High-Voltage Cable Lines

Prompt fault location in power cables with high accuracy is a measure aimed at achieving more reliable operation of the power grid system, in which the loss of electrical insulation strength is among the main factors causing accidents in the system. The article discusses the causes leading to faults in high-voltage cable lines and substantiates the relevance of determining the most effective fault location methods. Modern relative and absolute methods for cable line fault location are reviewed, and a comparative characteristic of the scope and limitations relating to the implementation of these methods is given. The article shows the expediency of using the acoustic method for fault location in cable lines as the most universal one, and also the burning method if the cable laying scheme is not available. The article also presents a comparative analysis of modern mobile electrical laboratories as the most advanced means for automating fault location in high-voltage cable lines, which makes it possible to reduce the fault detection time and ensure accurate cable line fault location. The necessity of improving the designs of mobile electrical laboratories and reducing their overall dimensions to improve the convenience of operation under urban conditions is substantiated.

Application of Augmented Spread Spectrum Time Domain Reflectometry for Detection and Localization of Soft Faults on a Coaxial Cable

Aviation cables are an essential part of aircraft, which transmits signals and power. Aviation cable faults often occur due to drastic changes in temperature and humidity and the effects of vibration. Obvious cable faults, such as open circuits and short circuits, can be detected during maintenance. However, some soft faults like the loose connection of the connector, damage of the insulation layer, and improper cable bending radius cannot be detected. Spread spectrum time domain reflectometry (SSTDR) is a diagnostic method for cable faults; however, the amplitude of the reflected signal is low when the soft fault occurs. SSTDR is only sensitive to apparent faults, but it is insufficient to detect soft faults. This article proposes an aviation cable fault detection and location method, augmented spread spectrum time domain reflectometry (ASSTDR), to detect soft faults effectively. First, the SSTDR results are decomposed into some intrinsic mode functions (IMFs) using the improved variational mode decomposition and selects the IMF with the highest kurtosis to analyze. Second, short-time Fourier transform (STFT) is applied to deal with the selected IMF to enhance the amplitude characteristics. Then, reassign spectrogram is computed to improve the resolution of STFT time-frequency distribution images to locate the location of soft faults. Finally, the effectiveness of ASSTDR is verified by experiments with different fault types and fault degrees. The comparative experiments demonstrate that the method can accurately detect soft faults.

Bidirectional LCL-T Resonant DC–DC Converter for Priority Loads in Undersea Distribution Networks

In undersea long-distance dc power distribution networks, power converters are distributed over the length of the trunk cable carrying constant dc current. Under regular circumstances, these series-connected isolated dc–dc converters deliver power from the constant current feed to their respective loads while regulating their output voltage or current. However, in the event of a cable or other faults, the converters in the string lose power from the source, and hence bidirectional load(s) could be employed for continuing power delivery to the priority loads. In this article, it is shown how a dual active bridge (DAB) based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> -T resonant converter can be designed to meet the requirements of driving such bidirectional loads to provide constant voltage to the load from a constant current source under regular operation and convert a voltage source to a constant current drive for the priority loads in the event of loss of power from the main feed. Steady-state modeling and analysis are furnished for the DAB <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> -T converter with three-angle modulation to derive all the dc and ac signals of the converter. Detailed design and modulation angle control are presented to minimize the overall VA rating of the resonant tank, transformer, and H-bridges. Experimental validation is provided for a prototype hardware system converting 1-A input current to 150-V output voltage in the forward direction and 1-A current drive from 150-V dc source in reverse power transfer, operating at 250-kHz switching frequency over a 50–500 W load range.

Fault Transient Study of a Meshed DC Grid With High-Temperature Superconducting DC Cables

This paper presents the DC fault transient study of a meshed DC grid with high-temperature superconducting (HTS) HVDC cables to integrate offshore wind power. A four-terminal meshed DC grid model with a ±100 kV DC voltage rating is developed. DC circuit breakers (DCCBs) are implemented in the DC grid to deal with DC cable faults. To conduct the fault transient study, the fault scenarios with different fault locations and protection delays are studied. The sensitivity study versus different DC fault locations reveals that the faults on the faulted cable will not cause the quenching of healthy cables. The sensitivity study considering different DC fault protection delays demonstrates that the HTS cables have a current-limiting effect and the long delay of DC fault protection does not result in large fault current and the quenching of healthy cables. Extensive simulations in PSCAD/EMTDC validate the founding.

Research on the Application of Uncertainty Quantification (UQ) Method in High-Voltage (HV) Cable Fault Location

In HV cable fault location technology, line parameter uncertainty has an impact on the location criterion and affects the fault location result. Therefore, it is of great significance to study the uncertainty quantification of line parameters. In this paper, an impedance-based fault location criterion was used for an uncertainty study. Three kinds of uncertainty factors, namely the sheath resistivity per unit length, the equivalent grounding resistance on both sides, and the length of the cable section, were taken as random input variables without interaction. They were subject to random uniform distribution within a 50% amplitude variation. The relevant statistical information, such as the mean value, standard deviation and probability distribution, of the normal operation and fault state were calculated using the Monte Carlo simulation (MCS) method, the polynomial chaos expansion (PCE) method, and the univariate dimension reduction method (UDRM), respectively. Thus, the influence of uncertain factors on fault location was analyzed, and the calculation results of the three uncertainty quantification methods compared. The results indicate that: (1) UQ methods are effective for simulation analysis of fault locations, and UDRM has certain application prospects for HV fault location in practice; (2) the quantification results of the MCS, PCE, and UDRM were very close, while the mean convergence rate was significantly higher for the UDRM; (3) compared with the MCS, PCE, and UDRM, the PCE and UDRM had higher accuracy, and MCS and UDRM required less running time.

A novel fault diagnosis method of power cable based on convolutional probabilistic neural network with discrete wavelet transform and symmetrized dot pattern

To accurately diagnose the XLPE power cable insulation fault, this research proposed a novel hybrid algorithm combined with Convolutional Probabilistic Neural Network (CPNN) based on Discrete Wavelet Transform (DWT) and Symmetrized Dot Pattern (SDP) analysis. First, it built seven different power cable insulation defect models to measure partial discharge signals of power cable insulation faults. Then, a discrete wavelet transform was used for noise filtering. The time-domain partial discharge signal was directly converted into the point coordinate feature image of visual polar coordinates by SDP analyses. Finally, the feature image was trained and recognized by CPNN. After the important feature information of the feature-image was extracted by convolution layer and pooling layer operations, it is applied to the power cable insulation fault state diagnosis system based on the rapid learning and highly parallel computing of Probabilistic Neural Network (PNN). The experimental results proved that the method proposed in this research could accurately diagnose the power cable insulation fault type and the recognition accuracy is higher than 96%. The proposed method has a short detection time and high diagnostic accuracy. This proves that it can be applied to detect the power cable insulation fault type.

Design of cable pipeline fault location device based on dynamic laser scanning

Aiming at the problem that some of the spare power cable pipelines without cable are damaged, and there is no effective detection equipment to detect the blocked state of the pipeline. This paper proposed a dynamic laser scanning method for obstacles in the pipeline, and designed a power pipeline fault positioning device. The infrared laser ranging technology based on time-of-flight is used to dynamically detect the distance between obstacles in the pipeline and the laser sensor, so as to detect the status and location of pipeline blockage. Taking ARM microcontroller as the core, equipped with VL53L0X laser module, the hardware design of the device was completed, and the software program was completed based on the Keil μVision5 IDE environment. The measurement and error analysis of obstacles of two different materials are carried out. The results show that the longer the measurement distance, the larger the error, and the highest measurement error is 1.47%, which fully meets the needs of engineering measurement.

Hybrid Methodology Based on Symmetrized Dot Pattern and Convolutional Neural Networks for Fault Diagnosis of Power Cables

This study proposes a recognition method based on symmetrized dot pattern (SDP) analysis and convolutional neural network (CNN) for rapid and accurate diagnosis of insulation defect problems by detecting the partial discharge (PD) signals of XLPE power cables. First, a normal and three power cable models with different insulation defects are built. The PD signals resulting from power cable insulation defects are measured. The frequency and amplitude variations of PD signals from different defects are reflected by comprehensible images using the proposed SDP analysis method. The features of different power cable defects are presented. Finally, the feature image is trained and identified by CNN to achieve a power cable insulation fault diagnosis system. The experimental results show that the proposed method could accurately diagnose the fault types of power cable insulation defects with a recognition accuracy of 98%. The proposed method is characterized by a short detection time and high diagnostic accuracy. It can effectively detect the power cable PD to identify the fault type of the insulation defect.