Cable Defects Research Articles

Diagnosis and Location of Cable Defects Based on Digital Reconstruction of Impedance Spectrum Under Pseudotrapezoidal PFM Excitation

Because the output voltage of the existing high-frequency instrument for the defect detection of long-scale transmission cable is too low, it is necessary to output both high-frequency and high-voltage excitation. A diagnosis and location method of cable defects based on pseudo trapezoidal pulse frequency modulation excitation and impedance spectrum digital reconstruction is proposed. A high-capacity pseudo trapezoidal excitation system based on SiC high-speed inverter is designed to measure the impedance spectrum up to the highest frequency of 7 MHz. Through digital reconstruction, the impedance spectrum with different cable defects can be obtained by the bandwidth extension within the range of 100 kHz - 63 MHz. Compared with traditional low-voltage sinusoidal excitation, the location results using the proposed time-space conversion function have a more minor interval oscillation and a faster convergence speed. The accuracy of defect location is improved by 80%, which verifies the effectiveness and accuracy of the proposed method.

K-mer Frequency Encoding Model for Cable Defect Identification: A Combination of Non-Destructive Testing Approach with Artificial Intelligence

This paper describes a non-destructive detection method for identifying cable defects using K-mer frequency encoding. The detection methodology combines magnetic leakage detection equipment with artificial intelligence for precise identification. The cable defect identification process includes cable signal acquisition, K-mer frequency encoding, and artificial intelligence-based identification. A magnetic leakage detection device detects signals via sensors and records their corresponding positions to obtain cable signals. The K-mer frequency encoding method consists of several steps, including cable signal normalization, the establishment of K-mer frequency encoding, repeated sampling of cable signals, and conversion for comparison to derive the K-mer frequency. The K-mer frequency coding method has advantages in data processing and repeated sampling. In the identification step of the artificial intelligence identification model, an autoencoder model is used as the algorithm, and the K-mer frequency coding method is used to introduce artificial parameters. Proper adjustments of these parameters are required for optimal cable defect identification performance in various applications and usage scenarios. Experiment results show that the proposed K-mer frequency encoding method is effective, with a cable identification accuracy rate of 91% achieved through repeated sampling.

Insulation aging diagnosis and defect location of crosslinked polyethylene cable in the distribution network based on radio frequency identification

With the expansion of power grid construction, the regulatory requirements of various equipment are increasing. Further, the safe and stable operation of the distribution network plays a crucial role in the entire power system. In particular, the cables in DNs are prone to aging during long-term operation due to various factors, such as electricity and heat. Therefore, the effective diagnosis of their operating status is necessary. Crosslinked polyethylene (XLPE) is widely used as the main insulation material for cables in power systems. In this study, the microscale and macroscale performance of the molding process of XLPE cables were compared. Wireless radio frequency identification (RFID) technology was used to diagnose the aging and locate the defects of XLPE cables in the DN. The experiment noted the average insulation crosslinking degree of 88.93% and 87.98% for the suspension chain- and tower-type XLPE cables, respectively. The insulation-melting and crystallization temperatures of the suspension chain-type XLPE cable were approximately 110 °C and 92 °C, respectively, and the crystallinity was approximately 36.5%. The insulation-melting and crystallization temperatures of the tower-type XLPE cable were approximately 105 °C and 88 °C, respectively, and the crystallinity was approximately 34%. Using the wireless RFID technology, the precise positioning of the local aging and insulation damage defects in XLPE cables can be achieved. The error in locating the cable defects using the high-frequency phase spectrum of the cable was less than 0.5 m. Therefore, the study on the manufacture of XLPE cables has significant impact on the physical and chemical properties of their insulation layer. Further, the cable inspection using the wireless RFID technology provides a guarantee on the safe operation of the power system.

Improvement of FDR Methods for Identifying Defects of Jointed HV Cable

This paper considers frequency domain reflectometry (FDR) as an effective way to locate high voltage (HV) cable defects. The formula relating FDR to cable local equivalents impedance value is presented, and critical methods such as window function, frequency step, and bandwidth to process FDR data before Inverse fast fourier transform (IFFT) are also demonstrated. Finally, a multi-periods reflection analysis from the IFFT curve is introduced with illustrations from fields measurement to improve the identification of defects for cables with multiple joints.

Non‐Destructive Testing of Bridge Cables using MIT

AbstractThis article presents the importance of non‐destructive testing (NDT) in maintaining the safety and functionality of bridge cables, and focuses on the practical use of magnetic‐inductive testing (MIT) and ultrasonic testing (UT) for NDT of bridge cables in free length and in the area of the end connectors. MIT is a non‐invasive method that can effectively detect defects such as broken wires, corrosion symptoms, and other imperfections, making it a promising technique for inspecting and maintaining bridge cables. A research project investigated various types of magnetic induction testing on different tension members made of single wires and evaluated the advantages and limitations of the MIT method. The study also explored the impact of factors such as cable diameter and measurement speed on the effectiveness of the technique. The results suggest that the MIT method can accurately detect defects in bridge cables and provide valuable information for maintenance and repair purposes. However, the article highlights the need for further research to optimize the MIT technique, particularly in automating the detection and characterization of signals, and to expand its applicability to a wider range of cable diameters and conditions.

Effect of Interface Defects on the Harmonic Currents in Distribution Cable Accessories under Damp Conditions

Dampness is one of the most important factors leading to the degradation of the insulation performance of cable accessories; it may easily lead to interface defects of composite insulation. In this paper, an electromagnetic field defect model of the cable composite insulation interface is established, and the effects of the defect degree and the defect type on the magnetic flux and the harmonic current are studied. The results show that the magnetic field is uniformly distributed with a decreasing trend when the cable is in good condition, and the grounding current has no obvious harmonic component. For different defects, the magnetic field changes at the defect location are notably different; for water droplets and the water film, the magnetic field distortion is obvious at the location of the defect and sheath, and for water tree defects, the distortion of the magnetic field is significant. In addition, the degree of distortion caused by the same defect at different positions is also different, and the magnetic field contrast is not obvious at different degrees of moisture exposure. The distortion of the grounding current caused by the water tree is higher than that of the water droplet and the water film, and the characteristics of the harmonic components of the grounding current caused by different defects have obvious distinctions. The type of cable defect can be judged by the proportion of harmonic components. This work can be used as a reference for the assessment of the cable insulation defect status.

Gabor wavelet transform combined with area CNN in appearance intelligent detection of stayed cables

The major structure for transmitting forces in a cable-stayed bridge is the stay cable. If the stay cable system fails, the whole bridge will collapse. At present, there is no very mature technical means for the detection of bridge cables. The most common method is for workers to perform regular manual inspection and replacement of cables. Although the defect condition of the cable can be detected to a certain extent, the real-time monitoring of its health cannot be realized. Therefore, the research used the median filter, mean standardization method and Retinex algorithm to preprocess the image data. And after extracting the stay cable picture’s macroscopic characteristics using the Gabor wavelet transform, it implemented image segmentation using the OTSU approach. The obtained feature images were applied to the Faster Regional Convolution Neural Network (RCNN) recognition model to detect microscopic defects. During the research, the training efficiency of traditional Convolution Neural Network (CNN) was not high. Therefore, Faster RCNN built a cable defect recognition model. Based on this, the research built an intelligent detection model for apparent defects of stay cables based on Gabor wavelet transform and improved RCNN. Through the experimental analysis, the model built for the study has a recognition accuracy rate of 94.14 %, which can achieve dynamic bridge health monitoring and instantly identify the condition of the stay cables, and maintain the safety of the bridge.

Partial Discharge Characteristics and Mechanism of Medium Voltage Power Cable Defects under Different Voltage Stresses

Off‐line partial discharge (PD) diagnosis, which requires an additional power supply has been accepted as an essential step in ensuring the integrity of power cable systems during commissioning testing. Many types of energizing voltage sources (e.g., alternating current resonance, damped alternating current, very low frequency [VLF] sine, and VLF‐cosine rectangular) have been developed, which are used in practice. However, choosing the optimal energizing voltage waveforms for PD testing is still controversial because their capability to motivate PD activities is still unclear. Accordingly, this paper compares the PD ignition capacity of the aforementioned power sources on six well‐designed PD defects in real cable joints. The influence of test voltage frequencies and waveforms are verified. The results show that the PD inception voltage and PD magnitudes in the cable joint defects do not change significantly as a function of the sinusoidal voltage frequency and the VLF‐cosine rectangular voltage exhibits excellent PD ignition capability with a low PD inception voltage and large PD magnitude. Moreover, the dynamic behavior of space charge in a PD defect caused by a gap is analyzed in order to explain the obtained results. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Comparative Study on Partial Discharge Characteristics of Seven Accessory Defects Under Four On-Site Testing Voltages of a 10-kV Cable Line

Due to the increase in voltage levels and line lengths of power cables, the commonly used alternating current resonance (ACR) voltage is challenging to meet the growth rate of the capacitance of ultra-high-voltage cables for the on-site partial discharge (PD) tests. PD research on power-frequency-like voltages such as damped alternating current (DAC) voltage, very low-frequency sine wave (VLF-Sine) voltage, and very low-frequency cosine rectangular wave (VLF-CR) voltage, has important engineering value for evaluating the cable insulation conditions. In this paper, a PD test and measurement system of a 10 kV XLPE cable line with a 2 km length is established. Seven typical accessory defects are made artificially, including insulation hole, insulation screen tip, ring-cut tool mark, and stress cone installation dislocation defects at the terminals, and connector tip, conductive impurity, and stress cone installation dislocation defects at the joints. PD tests under four kinds of testing voltages of ACR, DAC, VLF-Sine, and VLF-CR are carried out to compare the PD inception voltages (PDIV), phase-resolved PD (PRPD) patterns, and PD location results of various defects. PD equivalence between power-frequency-like voltages and ACR is characterized by PDIV equivalent coefficient <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PDIV</sub> , PRPD pattern characteristic quantities and discharge amplitudes. The results show that DAC and VLF-Sine have an outstanding ability to detect and characterize the severity of PD. DAC and VLF-CR have a good ability to recognize the type of PD. And DAC, VLF-Sine and VLF-CR can accurately locate PD. This study provides an important reference for on-site cable defect diagnosis and PD assessment.

Research on the Diffusion Model of Cable Corrosion Factors Based on Optimized BP Neural Network Algorithm

Corrosion factors enter the cable via diffusion and penetration from the defect position of the cable or the connection position between the anchoring system and the cable section, seriously affecting the cable’s durability. Exploring the transmission mechanism of corrosion factors in the cable structure is essential to reveal the durability and the long-term performance of the cable structure and to judge the corrosion damage of steel wires in the cable structure. Based on the machine learning (ML) method and the analytical solution of Fick’s second law, the laws between different temperatures, humidity, cable inclinations, cable defect areas, etc., and the diffusion coefficient of corrosion factors and the concentration of surface corrosion factors are obtained, also a spatial diffusion model of corrosion factors is established. According to the research, the optimum simulation result is achieved by employing the optimized back propagation (BP) neural network algorithm, which has a faster convergence speed and better robustness. Although ambient temperature, humidity, and corrosion time all impact the diffusion rate of corrosion factors, the tilt angle of the cable and the size of cable defects are the main factors influencing the diffusion coefficient of corrosion factors and the concentration of surface corrosion factors. The error between the concentration of corrosion factors calculated by the model in this article and the measured values at each spatial point of the cable is controlled within 15%, allowing for the spatial diffusion of corrosion factors to be effectively predicted and evaluated in practical engineering.

A method for online monitoring intermittent cable defects based on SSTDR

With the increase of cable operating age, various latent defects gradually appear, so it is important to identify and locate the defects accurately for power operation and maintenance. The common methods for cable failure detection primarily work with cables in an offline state, which causes a certain degree of economic loss. In this research, SSTDR is used to achieve online defect detection, which can avoid further damage to the cable in a working state. This paper established the numerical model of the cable distribution characteristics and investigated how various incident signals affect the effect of defect localization. Based on the obtained results, the localization curve is wavelet decomposed in order to filter the operating frequency signal. The findings of the experiments and simulations demonstrate that SSTDR and its enhanced algorithm may achieve online cable defect detection and serve as an effective method for cable defect locating technology advancement.

Multi‐type defect detection and location based on non‐destructive impedance spectrum measurement for underground power cables

AbstractPower cable defect detection and location is a crucial aspect in power asset maintenance. Impedance spectrum measurement can be used as a non‐destructive tool for cable monitoring. Defects from overheating and moisture ingress are investigated by the authors, and it is shown that multi‐type cable defects can be detected and located by this methodology. With both electromagnetic analysis and experimental investigation, the relation between impedance variation and the dielectric permittivity is studied to reveal the method's mechanism. An approach to implement such impedance measurement online is proposed and its perspectives in terms of sensitivity is investigated by field scale tests. It is shown that 2% impedance variation due to defect should be detectable. Field scale tests show perspectives for online application with signal coupling through a high‐frequency current transformer with focus on the differences in waveforms obtained from the impedance measurements.

Identification of XLPE cable insulation defects based on deep learning

The insulation aging of cross-linked polyethylene (XLPE) cables is the main reason for the reduction in cable life. There is currently a lack of rapid and effective methods for detecting cable insulation defects in power-related sectors. To this end, this paper presents a method for identifying insulation defects in XLPE cables based on deep learning algorithms. First, the principle of the harmonic method for detecting cable insulation defects is introduced. Second, the ANSYS software is used to simulate the cable insulation layer containing bubbles, protrusions, and water tree defects, and the effects of each type of defect on the magnetic field strength and eddy loss current of the cable insulation layer are analyzed. Then, a total of 10 characteristic quantities of the total harmonic content and 2nd to 10th harmonic currents are constructed to establish a database of cable insulation defects. Finally, the deep learning algorithm, long short-term memory (LSTM), is used to accurately identify the types of insulation defects in cables. The results indicate that the LSTM algorithm can effectively diagnose and identify insulation defects in cables with an accuracy of 95.83%.

Fast Detection Method on Water Tree Aging of MV Cable Based on Nonsinusoidal Response Measurement

A MV power cable will exhibit nonlinear impedance when dendritic defects appear. To solve the long time-consuming measurement of pure sinusoidal excitation and realize rapid and accurate evaluation of insulation conditions, a measurement and evaluation method based on nonsinusoidal excitation is proposed in this paper. Firstly, a nonsinusoidal excitation test system is established, which can obtain the high voltage dielectric response in the frequency range of 0.01 Hz–1 kHz. For parameterizing dielectric response characteristics of MV XLPE insulation under nonsinusoidal excitation, a Hammerstein-Wiener model is proposed to realize system parameter identification under non sinusoidal excitation with different aging degrees of water tree. The time-domain current response waveform based on the H-W model is more accurate than the traditional linear model, and the resulting fitting degree of the frequency-domain dielectric loss factor curve between the calculated H-W model and the measured curve was higher than 95%. The results show that the rotation and distortion of the u-i hysteresis ellipse under nonsinusoidal excitation can be used to diagnose the insulation defects of XLPE MV cables. The response model and its detection method proposed in this paper can improve the efficiency and accuracy of the field tests of cable insulation.

Convolutional Neural Networks for Defect Detection on LV cables

A convolutional neural network (CNN) is a machine learning algorithm that is particularly well-suited for tasks such as object recognition, image captioning, and speech recognition. CNNs are particularly effective at detecting features in images that are not easily observable by other machine learning algorithms, such as defects in manufacturing. By analyzing large collections of images, CNNs are able to find patterns that are indicative of defects. Power cables are an important part of manufacturing, as they allow machines to be operated and communicated with. Recently, great importance has been offered in making electricity generation, transmissions, distribution and storage smart. However, the shift to smart grid should include intelligent methods of detecting the reliability of electrical connections. Several types of electrical cables are used to transmission and distribution of electrical energy. Due to excellent electrical and mechanical properties, cross-linked polyethylene (XLPE) cables are widely used in power systems. Poor manufacturing techniques in the production and installation of cable joints will cause insulation defects. Some suggest, the use of interdigital capacitive (IDC) for online monitoring on XLPE cables. Others suggest The use of a continuous wave (CW) terahertz (THz) imaging technology could help display and detect interior faults in cross-linked polyethylene (XLPE) plates used for power line insulation. In this paper, I developed models which predominantly use locally collected custom dataset to forecast individual power cable physical safety status. The model is aimed at replacing the physical inspection with computer vision and image processing techniques to classify defective power cable from non-defective ones. The project is implemented using the Python programming language, the Tensorflow library, and a Convolutional neural network.The Convolutional Neural Network (CNN)-based method is purposefully chosen and applied in this project for power cable defect classification. The project culminates by recommending the use of same or additional datasets and provide modalities to detect power cable defect from live video.

Upper Sweeping Frequency Selection for Cable Defect Location Based on STFT

A frequency domain reflection (FDR) method has been widely used to locate cable defects. Generally, the performance of the FDR method can be enhanced by extending the sweeping frequency range of the signal. However, with the increase in the signal frequency, misdiagnosis can easily occur owing to the attenuation of the signal and noise. Thus, this paper’s authors propose the upper sweeping frequency (UF) selection method of FDR to improve the accuracy of cable defect location. First, the cable’s three-dimensional location spectrum, including the frequency and spatial information, is obtained at a wide frequency range based on short-time Fourier transform with excellent time-frequency aggregation characteristics. Second, the reasonable UF is selected through the energy change characteristics of the cable end. Then, the optimal localization result of the cable defect is calculated by interpolating the sum of energy among the effective frequency bands. Finally, two single-and multi-defect cases are used for the YJV-8.7/10kV XLPE power cable in the experiment to demonstrate the performance of the proposed method. The experimental results show that with the UF selection method, the impact of high-frequency signal attenuation on defect location is eliminated, and cable defect can be localized with more accuracy.

Anomaly Detection for Shielded Cable Including Cable Joint Using a Deep Learning Approach

A defect occurring in cable joints is much more severe than a defect on cables due to its high incidence and an intensive electrical stress. In conventional reflectometry, it is hard to distinguish between a reflected signal from normal cable joints and that from faulty cable joints. This paper proposes a novel time–frequency domain reflectometry (TFDR) method based on an unsupervised neural network model combining long short-term memory (LSTM) and variational autoencoder (VAE) that can detect joint defects as well as cable defects. To verify the proposed method, a test bed is constructed with two failure scenarios; (1) defects on cables, and (2) defects in cable joints. In both scenarios, the proposed method successfully detects the failure using an anomaly score that conventional TFDR does not have. The proposed anomaly detection technique is expected to become the cornerstone of systems that can detect anomalies of earlier-stage of defects.

Nondestructive Testing of Bridge Stay Cable Surface Defects Based on Computer Vision

The automatically defect detection method using vision inspection is a promising direction. In this paper, an efficient defect detection method for detecting surface damage to cables on a cable-stayed bridge automatically is developed. A mechanism design method for the protective layer of cables of a bridge based on vision inspection and diameter measurement is proposed by combining computer vision and diameter measurement techniques. A detection system for the surface damages of cables is de-signed. Images of cable surfaces are then enhanced and subjected to threshold segmentation by utilizing the improved local grey contrast enhancement method and the improved maximum correlation method. Afterwards, the data obtained through diameter measurement are mined by employing the moving average method. Image enhancement, threshold segmentation, and diameter measurement methods are separately validated experimentally. The experimental test results show that the system delivers recall ratios for type-I and II surface defects of cables reaching 80.4% and 85.2% respectively, which accurately detects bulges on cable surfaces.

Improved Locating Method for Local Defects in XLPE Cable Based on Broadband Impedance Spectrum

The crosslinked polyethylene (XLPE) cable safety is affected by environmental factors and artificial defects during operation. This work proposes an improved locating method based on broadband impedance spectrum (BIS) to locate local defects in XLPE cables. The calculation process of the algorithm has been analyzed. The selection of the incident Gaussian signal and the peak recognition method have been discussed, where the pulse width of the Gaussian signal was found to be determined primarily by the upper limit frequency of the traveling wave transmitting in the cable. The centroid and function fitting methods were established to reduce the peak recognition error caused by the test sampling rate. This work verified the accuracy of the algorithm through experiments. A vector network analyzer (VNA) was used to test the BIS of the cable. A 20 m-long cable containing abrasion and an inserted nail with different depths was measured in the BIS test. It was found that the abrasion and the nail could be located. The locating deviation of abrasion was within ±1%, and the centroid and function fitting methods could effectively reduce the locating deviation. The locating deviation was within ±1% when the depth of the nail inserted into the cable accounted for less than 50% of the insulation thickness. When the depth exceeded 75% of the insulation thickness, the deviation of each method was more significant, and the maximum absolute value of the deviation was 4%.

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.