Cross-linked Polyethylene Cables Research Articles

Maintenance Practices of High-Voltage Cross-Linked Polyethylene (XLPE) Cable Buffer Layers

As urbanization accelerates globally, the demand for electricity continues to rise, especially within high-voltage power transmission systems. Cross-linked polyethylene (XLPE) cables have become crucial due to their exceptional insulation properties and mechanical characteristics. However, the buffer layers of XLPE cables may undergo aging and faults caused by various factors, including electrical aging, thermal aging, chemical impacts, and improper design. This paper synthesizes existing literature to provide an in-depth analysis of the material properties, aging mechanisms, fault causes, design principles, and maintenance practices related to XLPE cable buffer layers. Through finite element method (FEM) simulations, it evaluates the electric field distribution across XLPE cables under different operating voltages and the contact state between the buffer layer and the metal sheath. Additionally, this study explores the recyclability and environmental impact of XLPE materials, as well as how improvements in material properties and detection technologies can enhance cable reliability and safety. The findings offer comprehensive theoretical and practical guidance to the power industry, aiding in optimizing cable design, improving system reliability, and reducing failure rates, while also suggesting future research directions.

Partial Discharge Signal Pattern Recognition of Composite Insulation Defects in Cross-Linked Polyethylene Cables.

To investigate the pattern recognition of complex defect types in XLPE (cross-linked polyethylene) cable partial discharges and analyze the effectiveness of identifying partial discharge signal patterns, this study employs the variational mode decomposition (VMD) algorithm alongside entropy theories such as power spectrum entropy, fuzzy entropy, and permutation entropy for feature extraction from partial discharge signals of composite insulation defects. The mean power spectrum entropy (PS), mean fuzzy entropy (FU), mean permutation entropy (PE), as well as the permutation entropy values of IMF2 and IMF13 (Pe) are selected as the characteristic quantities for four categories of partial discharge signals associated with composite defects. Six hundred samples are selected from the partial discharge signals of each type of compound defect, amounting to a total of 2400 samples for the four types of compound defects combined. Each sample comprises five feature values, which are compiled into a dataset. A Snake Optimization Algorithm-optimized Support Vector Machine (SO-SVM) model is designed and trained, using the extracted features from cable partial discharge datasets as case examples for recognizing cable partial discharge signals. The identification outcomes from the SO-SVM model are then compared with those from conventional learning models. The results demonstrate that for partial discharge signals of XLPE cable composite insulation defects, the SO-SVM model yields better identification results than traditional learning models. In terms of recognition accuracy, for scratch and water ingress defects, SO-SVM improves by 14.00% over BP (Back Propagation) neural networks, by 5.66% over GA-BP (Genetic Algorithm-Back Propagation), and by 12.50% over SVM (support vector machine). For defects involving metal impurities and scratches, SO-SVM improves by 13.39% over BP, 9.34% over GA-BP, and 12.56% over SVM. For defects with metal impurities and water ingress, SO-SVM shows enhancements of 13.80% over BP, 9.47% over GA-BP, and 13.97% over SVM. Lastly, for defects combining metal impurities, water ingress, and scratches, SO-SVM registers increases of 11.90% over BP, 9.59% over GA-BP, and 12.05% over SVM.

Study on the ablation process and failure mechanism of the buffer layer in high-voltage XLPE cable

In recent years, the frequent occurrence of ablation faults in the buffer layers of high-voltage cross-linked polyethylene (XLPE) cables has significantly undermined the stability of power grid operations. However, existing research cannot fully explain the failure mechanism and transformation of related physical and chemical properties of high-voltage cable buffer layer. To address this, the paper have constructed an integrated analysis framework that includes multiphysics field simulations, an equivalent electrical network model, and simulated ablation experiments to comprehensively reveal the complete ablation failure mechanisms. Firstly, this study conducted a comparative analysis of the voltage distribution and temperature changes in the buffer layer using electro-thermal coupling simulations and an equivalent electrical network model. This analysis provides a detailed study of the dynamic development process of buffer layer ablation and identifies key electrical parameters. Subsequently, it verified the accuracy of the simulations through experiments on temperature variation characteristics, changes in hydrogen content, and other gas concentrations by constructing a simulated buffer layer ablation experimental platform. Finally, based on the study of the mechanisms of the buffer layer ablation failure process, this study divided it into three stages: initial electrochemical corrosion, appearance of white powder, and the onset of ablation discharge in the buffer layer. The comparison of experimental results with simulation data from the first two stages further validated the effectiveness and robustness of our analysis framework. In summary, this study provides a comprehensive analysis of the failure process in high-voltage cable buffer layers through simulation and experimental validation. This study provides a methodological basis for preventing cable ablation faults and detecting and evaluating the status of high-voltage XLPE cable buffer layers.

Research on the Thermal Aging Characteristics of Crosslinked Polyethylene Cables Based on Polarization and Depolarization Current Measurement

Although XLPE cables are widely used in power transmission and distribution systems, their insulating properties are susceptible to degradation due to thermal aging. In order to clarify the influence law of the thermal aging process on the structural and dielectric properties of XLPE cables, this paper investigates the thermal aging characteristics of XLPE cables by using polarization and depolarization current measurement. Results show that when the XLPE cable is aged at 140 °C, the crystallinity of the insulation layer appears to increase and then decrease. With the increase in aging time, micron-sized microvoids appear on the surface of the XLPE. At the same time, the DC conductivity and 0.1 Hz dielectric loss factor of the insulating layer increase with the aging time. The average DC conductivity increased from 2.26 × 10−16 S/m for new cables to 4.47 × 10−16 S/m after aging for 432 h, while the dielectric loss increased from 0.11% to 0.42%. The polarization characteristics of thermal-aged cables were further analyzed using the extended Debye model. Results indicate that the time constant of the third branch of the model increased significantly with increasing aging time. A correspondence between this parameter and the thermal aging time of the cable was established. Thermal aging can damage the crystalline structure of XLPE, so that the number of interfaces between the crystalline and amorphous regions of the material increases, resulting in structural damages and a decline in the dielectric properties of the cable insulation.

A review: partial discharge in medium voltage cross-linked polyethylene XLPE cable

Power cables are one of the equipment that plays a vital role in the power system network, either in High Voltage (HV) or Medium Voltage (MV) network. To ensure a reliable power supply and distribution, the integrity and the condition of the cable system must be ensured. Partial Discharge (PD) diagnosis is a valuable tool to analyse and evaluate the condition of the insulation. The purpose of this paper is to review how PD activity is being utilized to assess the condition of the XLPE (cross-linked polyethylene) cables specifically in MV level where a review of existing PD test methodology that are used by researchers and engineers to diagnose the insulation condition of the cable will be presented.

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.

Chemical Property Evaluation and Tensile Strength Correlation of XLPE Insulators Based on Accelerated Thermal Aging

Cross-linked polyethylene (XLPE) cable is a representative power transmission cable. XLPE has excellent mechanical properties, chemical and heat resistance, and insulation. However, XLPE insulation deteriorates during operation due to electrical, mechanical, and thermal stresses. Among these, thermal stress is a major factor and reduces insulation properties due to a change in molecular structure. Therefore, XLPE characteristic evaluation by heat exposure is essential for power cable condition evaluation. Herein, deteriorated XLPE samples were characterized by tensile strength, X-ray diffraction, Fourier-transform infrared spectroscopy, differential scanning calorimetry, and ultraviolet-visible spectroscopy after exposure to various temperatures and durations. Comparing the tensile strength with other analysis results yielded correlations. Each characteristic showed a linear relationship. The correlation between tensile strength and carbonyl index was the strongest, and the coefficient of determination, R2, was 0.9299. Therefore, these results will provide important information on chemical properties when establishing operational management standards for XLPE insulators in the future.

Multiple Partial Discharge Signal Classification Using Artificial Neural Network Technique in XLPE Power Cable

According to partial discharge (PD) damage in the electrodes that are not entirely bridging, the presence of PD in the high voltage (HV) power cable might lead to insulation failure. PD defects can damage cross-linked polyethylene (XLPE) cables directly, which is one of the most critical electrical issues in the industry. Poor workmanship during cable jointing, aging, or exposure to the surrounding environment is the most common cause of PD in HV cable systems. As a result, the location of the PD signals that occur cannot be classified without identifying the multiple PD signals present in the cable system. In this study, the artificial neural network (ANN) based feedforward back propagation classification technique is used as a diagnostic tool thru MATLAB software in which the PD signal was approached to determine the accuracy of the location PD signal. In addition, statistical feature extraction was added to compare the accuracy of classification with the standard method. The three-point technique is also an approach used to locate PD signals in a single line 11 kV XLPE underground power cable. The results show that the statistical feature extraction had been successful classify the PD signal location with the accuracy of 80% compared to without statistical feature extraction. The distance between PD signals and the PD source affected the result of the three-point technique which proved that a lower error means a near distance between them.

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%.

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.

Comparative Analysis of XLPE and Thermoplastic Insulation-Based HVDC Power Cables

The application of cross-linked polyethylene (XLPE) cables to voltage sourced converter (VSC)-based high voltage direct current (HVDC) systems has already been technically verified and has become common, and thermoplastic (TP) is attracting attention as an insulation material for next-generation cables due to the recent development of material-related technologies. However, studies related to TP cables are mainly focused on improving material properties, and studies related to cable systems are insufficient. In this paper, XLPE and TP cables were designed for application to VSC-based HVDC systems, and major characteristics such as electric field distribution and thermal stability were compared and analyzed through overvoltage simulation. The insulation design method of HVDC cable was presented, and the design was performed using XLPE and TP insulation materials. The temperature and electric field profiles of the cables were also analyzed through a finite element method simulation. To analyze the performance of the designed cable, it was simulated with the PSCAD/EMTDC program. Based on the simulation results, the major characteristics of XLPE and TP cables were compared and analyzed. Results showed that in the case of TP cables, insulation properties were excellent, but thermal conductivity was relatively low; therefore, countermeasures are needed.

Assessing Aging Status and Type of XLPE Cable Insulation With a Graphic Approach Based on PDC Measurement

The polarization and depolarization current (PDC) measurement has been widely used to assess the aging degree of cross-linked polyethylene (XLPE) cables. However, the aging type identification of XLPE cables has not been fully considered. In this study we conduct PDC measurement on thermal-aged and water tree-aged XLPE cable samples respectively, upon which a graphical approach to assess the insulation condition and distinguish the aging type of XLPE cables is proposed. First, to designate the evaluation indexes of the insulation condition, multiple PDC measurement based dielectric parameters are considered. Then, to reflect the sensitivity of each evaluation index to the condition of XLPE cable, a combined weighting method based on the group fuzzy analytic hierarchy process (GFAHP) and entropy method (EM) is introduced. Furthermore, the improved radar chart method (RCM) based graphical approach is proposed to comprehensively and intuitively characterize the evaluation indexes. Finally, the aging degree of the XLPE cable samples is quantitatively assessed by calculating the area of the polygon in the chart. Simultaneously, utilizing the position of the areal coordinate and the shape characteristic parameter of the polygon, the thermal-aged and water tree-aged samples are effectively distinguished.

Using Asphalt as an Additive for Waste Cross-Linked Polyethylene Recycled Materials to Improve Thermoplastic Processing.

With good insulation, cross-linked polyethylene (XLPE) cables are widely used as an important basic material for power transportation. Due to being insoluble and infused, the cross-linked network structure caused a challenge in the recycling of waste XLPE, which is usually treated by incineration and landfilling. In this research, XLPE was part-de-cross-linked via solid-state shear milling (S3M) technology, but the resulting powder was difficult to process. In order to improve the re-processability of XLPE, asphalt with a similar structure was added during the thermoplastic processing. To deeply understand the influence of asphalt on the matrix, the compatibility, dispersion, and rheological properties of the composites were characterized. Due to the good compatibility between de-cross-linked XLPE and asphalt, the viscosity of the composites decreased significantly. Some sea-island structures also formed in composites, which increased the toughness of the composites, so the elongation at break reached as high as 322%. The use of asphalt to achieve the processing performance of part-de-cross-linked XLPE powder was highly effective. Furthermore, the prepared composites showed potential application in the field of waterproofing, which could recycle waste XLPE cables on a large scale.

Terahertz Non-Destructive Testing and Imaging of High-Voltage Cables

The evaluation of the internal defects in high-voltage cross-linked polyethylene cables is highly crucial for the security and reliability of power transmission. Here, a terahertz (THz) frequency-modulated-continuous-wave non-destructive testing (NDT) imaging system is used to demonstrate the non-contact detection of a high-voltage cable (35 KV). Combined with linear scanning and axial rotation, the three-dimensional (3D) data of the columnar target is acquired, and a 3D perspective image is reconstructed. The correspondence between the target in the real space and image space is determined to accurately show the internal spiral structure and strand number of metal wires. The results indicate that prefabricated air and water holes in the polyethylene insulation layer can be displayed. Furthermore, feature defect signals are automatically classified and recognised by combining the principal component analysis dimension reduction method and the support vector machine classification method, which can predict abnormal defects more efficiently in mass data. These results provide technical guidance for the non-contact NDT and visual evaluation of the internal state of detected cable targets.

Evaluation of Non-Uniform Thermal Aging of XLPE Cable Based on Modified Debye Model

The polarization/depolarization curve (PDC), which contains abundant insulation information, is widely used to evaluate the insulation condition of the cross-linked polyethylene (XLPE) cables. However, the insulation condition assessing the result of the XLPE cables is usually inaccurate using the PDC technology because the parameters of the current Debye model are determined in a uniform aging hypothesis, instead of a real non-uniform situation. To solve this problem, the modified Debye model considering the real non-uniform situation is proposed, and the effectiveness of the modified model is proved by the comparison of the PDC test results with non-uniform and uniform aging materials. It is found that the parameters of the extended Debye model tend to overestimate the cable insulation performance, while the characteristic parameters obtained by the modified Debye model better reflect the real situation of cable non-uniform aging. Result analysis shows that the proposed method has better accuracy in the insulation condition evaluation of cables.

Electrically Conductive and All-Weather Materials from Waste Cross-Linked Polyethylene Cables for Electromagnetic Interference Shielding

Electrically Conductive and All-Weather Materials from Waste Cross-Linked Polyethylene Cables for Electromagnetic Interference Shielding

Field Strength Simulation of Typical Defects in XLPE Cable

Crosslinked polyethylene (XLPE) cables are now widely used in power transmission. Due to various factors, XLPE cables are unavoidable to produce defects during installation, transportation and use. These defects can easily cause partial discharge and even lead to cable breakdown accidents, which can cause great losses. [1] In this paper, three typical defects in XLPE cables are studied: air gap defect inside XLPE insulation, metal spike defect on the surface of insulation layer and metal particle defect on the main insulation surface. Electric field simulation is carried out by COMSOL finite element software. Three corresponding cable models under 132kV DC voltage are established, and the simulation analysis of electric field strength is carried out. The field strength distributions for different defects are obtained. The results show that the overall electric field strength of the insulation defect decreases with the distance from the surface of the cable insulation layer. However, the distortion of the electric field strength at the defect is different according to the location and size of the insulation defect. It needs to be simulated under different conditions.

Ispitivanje specifične toplotne otpornosti i vlažnosti kablovske posteljice u laboratorijskim uslovima

This paper at first hand briefly presents examples of the maximum permitted current changes for one cable depending on the specific thermal resistance soil value rz. It is emphasized that the permanently permissible current load of the cable should be calculated using a constant value rz in design - Then, for two cases from field, two samples were examined. The first sample is undisturbed soil from the cable route and the second sample is specially prepared cable bed. The corresponding rz values were obtained using the needle probe method in the laboratory. The difference is pointed out between the measured values of specific thermal resistivity and moisture of the samples. Some samples were being dried in thermal chambers and the others were being dried on ambient temperature. For drying in thermal chambers, based on experience for cross-linked polyethylene cables, the temperatures of 60°C to 90°C were selected for the first case while in the second case the temperature of 20°C over a long period of time was applied. The results obtained were analyzed and shown in appropriate tables and graphs. At the end of this paper the moisture of the soil was calculated for sampled cable bed that corresponds to the value of rz of 1(Km/W). The composition of the cable bed that allows the designed value of the rated power of the cable to be achieved is proposed.

Remaining lifespan prediction of cross‐linked polyethylene material based on GM(1, N) grey models

Cross-linked polyethylene (XLPE) cables are a critical component of power transmission systems. As operating conditions sometimes exceed the thresholds set in discipline, some cables fail to reach their designed lifespan, and consequently evaluating the aging degree of cables is particularly necessary. Here ten cables with an operating duration between 1 and 11 years from adjacent lines were sampled. Investigations were performed through various aging evaluation methods. Then typical lifespan prediction indices were concluded from test results and filtrated through correlation calculation. Meanwhile, a lifespan prediction method based on GM (1, N) model was proposed to establish the equivalent relationship between operating time and these indices. The research results indicated that, lifespan prediction indices obtained were all significantly correlated with operating time, which can be used to characterize aging degree of XLPE cables. Besides, due to the high prediction accuracy of experimental verification, the GM (1, N) model was proved to be applicable to evaluate the aging degree quantitatively and determine the remaining lifespan (relative to the designed lifespan) of XLPE cables. In future research, this method can be used to formulate a reasonable and economic withdrawal strategy for XLPE cables operating in conditions that exceed the thresholds set in the discipline.

THE ABILITY OF MODERN TECHNOLOGIES TO INFLUENCE THE NEUTRAL MODE OF ELECTRIC GRIDS

In this article, we consider the modes of neutrals of 0,38 to 750 kV networks. In any network with different voltage classes, the neutral point of a power transformer winding can operate both in isolated and deaf earthed mode. However, the choice of this mode depends on economic feasibility.
 The paper presents arguments and advantages of application of system with isolated and compensated neutral of networks with voltage classes 6–35 kV. For networks of these voltage classes various options of connecting arc suppression reactors with step and smooth regulation, as well as low and high impedance resistors in the neutral point of neutralizing transformer or zero sequence filter are considered. On the example of operation of electric networks of the Republic of Chuvashia for many decades it is emphasized that compensation of capacitive currents with arc suppression reactors is an effective and reliable way to protect not only substation electrical equipment from overvoltage, but also to ensure electrical safety of people and animals, as well as reinforced concrete towers from destruction.
 It is noted that the 6–35 kV networks in the future can be made with deaf earthed neutral. However, this will be possible under the condition that self-supporting insulated wires will be installed on overhead transmission lines instead of bare wires, and cross-linked polyethylene cables will be used on cable lines. It is emphasized that the introduction of work under voltage in 6-35 kV networks and digitalization of substations with the widespread introduction of microprocessor technology will accelerate the transition from a system with insulated (or compensated) neutral to a deaf earthed system. Nevertheless, the decision to switch from the insulated neutral system to a deaf earthed system will remain the prerogative of the design organization based on the specifics of the electrical equipment and the sphere of industrial activity.