Cable Partial Discharge Research Articles

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.

Improving power cable partial discharge pattern recognition through gustafson-kessel fuzzy clustering techniques

Existing methods for recognizing partial discharge patterns in power cables do not utilize fuzzy clustering of the discharge signals, resulting in poor quality and low recall and precision of the pattern recognition. To address this, we propose a new approach for partial discharge pattern recognition in cables using Gustafson-Kessel(GK) Fuzzy Clustering. The method involves acquiring signals from a power cable partial discharge monitoring system and then processing the signals with GK fuzzy clustering. The clustered discharge signals are filtered with wavelet packet transforms before input into an improved adaptive resonance theory(ART) neural network for final pattern recognition. Experiments demonstrate the new technique achieves up to 98.7% recall and 85.6% precision for discharge pattern recognition, with discharge signal Signal Noise Ratio(SNR) between 55 dB and 62 dB and maximum recognition accuracy reaching 98%. The proposed fuzzy clustering-based pattern recognition approach significantly enhances partial discharge diagnostics for power cable monitoring.

Fault Feature Mining and Identification Method of 10kV XLPE Cable Combining SAE and RF

Background: Feature extraction methods such as statistical features and image features based on traditional partial discharge spectrograms require the support of expert experience and prior knowledge, making their engineering applications have significant limitations. Currently, electrical equipment fault diagnosis is moving towards intelligent and scientific directions, and how to apply deep learning methods for more accurate, intelligent, and autonomous diagnosis has become a hot issue in the current field. background: Feature extraction methods such as statistical features and image features based on traditional partial discharge spectrograms require the support of expert experience and prior knowledge, making their engineering applications have significant limitations. Methods: The construction and optimal selection of high-voltage cable partial discharge feature space is an important means to improve recognition accuracy. This paper proposes a new method that can independently and deeply mine partial discharge image features to quickly identify partial discharge types. Firstly, a self encoder is used to encode and decode partial discharge images, and a multi-level stack structure is used to achieve the goal of deep feature mining; then, the partial discharge data features decoded based on a stack self encoder are imported into a random forest network for type recognition; Finally, based on the random forest algorithm, the redundant feature space is optimized and compared with other traditional recognition methods. Results: The recognition accuracy of the proposed method is about 10% higher than that of traditional methods. Conclusion: The optimized feature parameters can maintain more than 90% recognition accuracy in both random forest algorithms, common support vector machines, and BP neural networks, proving the feasibility and effectiveness of the method in this paper. result: The recognition accuracy of the proposed method is about 10% higher than that of traditional methods.

Diagnostics analysis of partial discharge events of the power cables at various voltage levels using ramping behavior analysis method

Partial discharge events can occur in high-voltage cables. It can be caused by defects in the cable insulation, contamination, or a combination of both. Partial discharge in cables can lead to insulation failure and cable failure. This investigation aims to identify the trends and patterns in the internal partial discharge (PD) occurrences in the power cables when exposed to different voltage levels - 6.4, 7.4, 9.4, and 11.3 kV. For pattern extraction, a well-established method, ramping behavior analysis, is implemented to extract and classify PD occurrences into sets of significant and stationary events. In this investigation, significant events correspond to an absolute peak (the discharge pulse) and subsequent oscillations from the measurement sensor. The stationary events represent a collection of noise that is recorded during the measurement. These noise signals are essentially small variations within a pre-determined threshold range. Furthermore, a comparative analysis is performed for each voltage level and for the voltage levels.This investigation brings new knowledge on how internal partial discharge pulses occur at various voltage stress levels. Specifically, the emerging patterns and trends of internal partial discharge events. The results indicate that there is a positive correlation between the number of PD events and the increase in stress levels. Furthermore, negative PD peaks are more frequent at lower stress levels.

Noise Reduction of Cable Partial Discharge Vibration Signal based on EEMD-CIIT method

Abstract The cable partial discharge vibration signal is often polluted by noise. It is necessary to denoise the signal, improve the signal-to-noise ratio, and then realize the feature extraction of the vibration signal. Therefore, this paper builds a cable partial discharge test platform, collects the time-domain waveform of vibration signals under different working conditions, analyzes the noise reduction effect of ensemble empirical mode decomposition clearing repetition interval threshold noise reduction method under different threshold functions, and analyzes the difference and approximation degree between the simulated signal and the real signal after noise reduction by using signal-to-noise ratio and correlation, By comparing the difference of statistical characteristics in different defects before and after noise reduction, the wavelet threshold denoising method is established synchronously and compared with the proposed method. The results show that the high-frequency part of the vibration signal has obvious random noise components, and the effective frequency is mainly concentrated in the low-frequency band; The smoothing shear absolute deviation penalty threshold function has the smoothest curve after noise reduction and the highest approximation to the real signal. The high-frequency noise component is eliminated, and the low-frequency band frequency component is effectively retained. The method proposed in this paper can be extended to the noise reduction of vibration signals of other industrial equipment and provide technical support for the signal noise reduction of various industrial equipment.

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.

Distributed Detection and Acoustic Emission Waveform Retrieval of Cable Joint Partial Discharge

Distributed detection and acoustic emission (AE) waveform retrieval of partial discharge (PD) in power cable joints are significant but challenging tasks. In this paper, we proposed a cable joint PD detection method based on optical fiber distributed acoustic sensing (DAS). Ordinary single-mode optical fiber (SMF) was wound on the outer semiconductive layer to obtain sufficient sensitivity. Coherent optical time domain reflection (COTDR) principle was utilized to detect the phase variation and locate the PDs. The AE waveforms were retrieved by Fourier integration and difference operation. The experimental results indicated that the sensing system had the ability of multi-point monitoring. The PD AE waveforms caused by conductor spike and insulation air gap defects were accurately retrieved.

Feature Extraction and Pattern Recognition Algorithm of Power Cable Partial Discharge Signal

The degree of insulation aging of power cables is closely related to their partial discharge (PD) level, so the analysis of PD signals can be used to realize the cable condition detection. However, after performing online detection of PDs on power cables, the collected signals always contain interference signals due to the influence of electromagnetic interference in the field. In order to identify each type of local discharge signal from the interference signal, this paper proposes a clustering identification algorithm for local discharge signals, which mainly involves pulse extraction, feature parameter extraction and clustering identification process. The algorithm first extracts the pulse signal by combining the amplitude–time threshold method and the time domain energy method, then obtains the feature vector of the signal according to the synchronous multi-channel method, designs a fuzzy C-mean clustering algorithm based on subtractive clustering to determine the initial clustering center to cluster the samples and finally analyzes and checks the clustering results according to the phase resolved PD (PRPD) of a single class of signals and the fit of the two-parameter Weibull distribution function. The clustering results were analyzed and examined. The experimental results show that the proposed algorithm can extract pulse signals efficiently and accurately, and the synchronous multi-channel method can characterize pulse signals better. Meanwhile, the algorithm can determine the optimal number of classes adaptively according to the clustering effectiveness function and adopt subtractive clustering to initialize the clustering center, which can approach the optimal solution faster, and can effectively cluster a variety of discharge signals, so as to realize the type identification of single-class discharge signals.

A Denoising Method for Mining Cable PD Signal Based on Genetic Algorithm Optimization of VMD and Wavelet Threshold

When the pulse current method is used for partial discharge (PD) monitoring of mining cables, the detected PD signals are seriously disturbed by the field noise, which are easily submerged in the noise and cannot be extracted. In order to realize the effective separation of the PD signal and the interference signal of the mining cable and improve the signal-to-noise ratio of the PD signal, a denoising method for the PD signal of the mining cable based on genetic algorithm optimization of variational mode decomposition (VMD) and wavelet threshold is proposed in this paper. Firstly, the genetic algorithm is used to optimize the VMD, and the optimal value of the number of modal components K and the quadratic penalty factor α is determined; secondly, the PD signal is decomposed by the VMD algorithm to obtain K intrinsic mode functions (IMF). Then, wavelet threshold denoising is applied to each IMF, and the denoised IMFs are reconstructed. Finally, the feasibility of the denoising method proposed in this paper is verified by simulation and experiment.

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.

FoSSA Optimization-Based SVM Classifier for the Recognition of Partial Discharge Patterns in HV Cables

In order to enhance the classification accuracy and the generalization performance of the SVM classifier in cable partial discharge (PD) pattern recognition, a firefly optimized sparrow search algorithm (FoSSA) is proposed to optimize its kernel function parameters and penalty factors. First, the Circle-Gauss hybrid mapping model is employed in the population initialization stage of the sparrow search algorithm (SSA) to eliminate the uneven population distribution of random mapping. Sparrows tend to fall into local extremums during the search process, while the firefly algorithm has a fast optimization speed and strong local search ability. Thus, a firefly disturbance is added in the sparrow search process, and the fitness value is recalculated to update the sparrow position to enhance the sparrow's local optimization ability and accuracy. Finally, based on the SSA, a dynamic step-size strategy is adopted to make the step size dynamically decrease with the number of iterations and improve the accuracy of convergence. Six benchmark functions are employed to evaluate the optimization performance of the FoSSA quantitatively. Experiment results show that the recognition accuracy of the PD patterns using the SVM optimized by the FoSSA could reach 97.5%.

Research on characteristics of acoustic signal of typical partial discharge models

The pulse current method, acoustic and ultrasonic partial discharge (PD) detection, and voiceprint PD detection are commonly used detection methods for the PD detection of power equipment. To study the characteristics of PD signals of typical discharge models based on the principles of the above three detection methods, an acoustic detection experimental system consisting of a needle-tip model and a surface model was built. Acoustic tests were carried out on needle-tip models with different curvature radii and surface discharge models with different lengths of conductive paste. The experimental results showed that acoustic and ultrasonic PD detection and voiceprint PD detection exhibited different sensitivities to the needle-tip discharge models, and the combination of acoustic and ultrasonic PD and voiceprint PD detection was more beneficial for the comprehensive detection of cable PD signals. Based on voiceprint recognition technology, this study drew FFT (Fast Fourier Transformation) diagrams of different types of PD acoustic signals and analyzed the differences in the ultrasonic signal frequency distribution. The frequency band of the voiceprint PD signal of the needle-tip discharge models was concentrated in the range 17–27 kHz, and the frequency band of the voiceprint PD signal of the conductive paste discharge models was concentrated in the range 20–25 kHz. The measurement of voiceprint PD signals in these frequency bands were strengthened when the PD of a cable was detected on-site, which provides the basis for the use of the cable model for on-site PD detection.

Research on cable partial discharge detection and location system based on optical fibre timing

Partial discharge (PD) is an important index to reflect the running state of cables. According to the characteristics and propagation mechanism of cable partial discharge signal, a cable partial discharge detection and location system based on optical fibre time synchronisation technology and travelling wave double terminal location principle is developed. The system has high detection sensitivity, high reliability, real-time detection, diagnosis and positioning of cable discharge power supply. The experimental results show that the positioning accuracy of the cable partial discharge source can be effectively improved based on the fibre timing and double terminal positioning technology, and the positioning accuracy can reach 1%; the method studied in this paper can meet the requirements of the accurate location of the partial discharge source of the cable, Gil and other equipment.

Research on cable partial discharge detection and location system based on optical fibre timing

Research on cable partial discharge detection and location system based on optical fibre timing

Voltage Equivalence of Partial Discharge Tests for XLPE Insulation Defects

Partial discharge (PD) is an important factor leading to insulation deterioration and fault of power cables. Due to large cable capacitances, power supplies should provide high reactive charging currents during field tests; common PD test methods of power frequency (PF) voltage or alternating current resonance (ACR) voltage cannot meet the field test requirements with the increase of cable voltage levels and line lengths. At present, the equivalence between PF-like (PFL) voltages, such as damped alternating current (DAC) voltage, very low-frequency sine (VLF-Sine) wave voltage, or very low-frequency cosine rectangular (VLF-CR) wave voltage and PF is still unclear. In this article, four kinds of defect flat models of the needle tips, air gaps, metal particles, and moisture are made. The PD tests are carried out under different voltage waveforms [DAC, sine, and cosine rectangular (CR)] and frequencies (0.1&#x2013;1000 Hz). The PD inception voltages (PDIV), phase-resolved PD (PRPD) spectra, and PD amplitudes of various defects are studied. The PDIV&#x2019;s equivalent coefficient <inline-formula> <tex-math notation="LaTeX">${K}_{\text {PDIV}}$ </tex-math></inline-formula>, total discharge amplitude&#x2019;s equivalent coefficient <inline-formula> <tex-math notation="LaTeX">${K}_{\textit {W}}$ </tex-math></inline-formula>, and PRPD&#x2019;s statistical operators are proposed to characterize the PD equivalence between PFL and PF quantitatively. The results show that <inline-formula> <tex-math notation="LaTeX">${K}_{\text {PDIV}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${K}_{W}$ </tex-math></inline-formula>, and PRPD&#x2019;s statistical operators can better evaluate the PD discovery abilities, PD severities, and PD-type recognition abilities of different voltage types. This study provides a technical basis for selecting the appropriate test voltage types and ensuring the test scientificity and reliability of cable PD tests in the field.

Review on Detection and Analysis of Partial Discharge along Power Cables

Partial discharge (PD) detection and analysis plays a crucial role for acceptance testing and condition monitoring of power cables. Various aspects are related to PD in power cables from theory to practice. This paper first summarizes the PD mechanism and models used for PD analysis in power cables. Afterwards, PD detection is addressed in the aspects of off-line test, on-line test, and sensors. PD analysis is discussed in detail. Specifically, related quantities and algorithms for PD analysis are outlined. PD characteristics with affecting factors, e.g., dielectric type, load, and applied voltage are discussed. Experience on PD development trend with measurements in field is analyzed. Based on the comprehensive review, challenges of PD detection and analysis along a power cable are proposed.

Partial Discharges in HVDC Cables - The Effect of the Temperature Gradient During Load Transients

The paper investigates the role of the space charge accumulation phenomena in the inception of Partial Discharges (PD) in a defect within the bulk of the dielectrics of a High Voltage Direct Current (HVDC) cable. It is widespread accepted that the number of PD occurring in an HVDC cable during its lifetime is largely less than that under AC voltage stress. This is essentially due to higher values of the voltage between the conductor and the screen needed to trigger PD under a steady DC voltage than the same value under AC stress. Nevertheless, due to the dependency of the electrical conductivity of the insulation on the electric field and temperature, PD in HVDC cables is strongly influenced by the temperature distribution and, consequently, on the load current. Thus, the main goal of this research is to evaluate the main parameters of the PD occurring in an HVDC cable during a load transient. Numerical simulations by using Comsol Multlphysics® are validated by the results obtained with an ad hoc experimental setup. A model DC cable, subjected to transient phenomena due to a current step and with an air void defect in the insulation material, is simulated and setup. On the other hand, an experimental setup is used to carry out PD measurements on the same case study. The results of this research show the strong dependency of the PD parameters on the temperature distribution varying over time during a load transient.

Исследование характеристик частичных разрядов в СПЭ-изоляции кабеля с множественными дефектами

The paper simulates the electric field intensity picture in the cable with one and several defects. Modeling has shown that the presence of several closely spaced defects does not lead to an increase in the electric field intensity in comparison with one defect. On the basis of experimental studies, an analysis of defects' number influence on various parameters of partial discharges (PD) is performed. Artificial defects are created in the area of the XLPE-insulated cable termination. The following parameters of PD are measured: the magnitude of the apparent charge, the power (intensity) of partial discharges, the phase-resolved partial discharge (PRPD) patterns, the shape of PD signals. The analysis shows an increase in the number of defects does not affect the characteristics of partial discharges, despite the fact the defects are located in different sections of the cable with distributed parameters. Thus, it can be difficult to quantify defects during cable partial discharge measurements.

Fault Diagnosis Based on Multiscale Texture Features of Cable Terminal on EMU of High-Speed Railway

The problem to identify the type of source generating discharges detected during campaigns of partial discharge (PD) measurements in cable terminal is of utmost importance in the quality control and state detection of the power supply system of a high-speed railway train. The purpose of this article is to provide a novel PD feature and a potential tool for cable terminal PD identification. The traditional PD type of high-speed train cable terminal is usually identified by PD test parameters, such as amplitude, PD times, and discharge energy. These features contain limited information and are easily affected by external factors, resulting in large numerical fluctuations, making the reliability of the test results low, which makes the operation, maintenance, and repair decision of high-speed train cables very difficult. Therefore, this article proposes a method that can extract rich feature information from PD data and distinguish PD types accurately and reliably by combining with a classification algorithm. First, to make full use of the characteristics of PD data, the single-scale PRPD images are transformed into multiscale PRPD images by using image pyramid theory. Then, the first-order texture statistics, the second-order texture statistics, and the higher order texture statistics are extracted from the multiscale PRPD space. Finally, the input features are optimized and classified by random forest and compared with other feature extraction algorithms. The results show that this method can identify PD type accurately and reliably, and the recognition accuracy is better than that of a single PD discharge feature.

Application and verification of partial discharge measurement system for power cable based on oscillating wave voltage

Abstract Compared with conventional methods for insulation performance evaluation of power cables, the oscillating wave test system used in partial discharge measurement for power cables has advantages of high integrity, easy operation, low power consumption, and compact size in addition, partial discharge, defect localization, and dielectric loss can be measured simultaneously without any damage to cable insulation. Therefore, the oscillating wave test system has been widely applied for insulation performance evaluation of newly installed as well as fault power cables. However, there is no study so far on the verification method of oscillating wave test system. This paper dealt with the application and verification for cable partial discharge measurement devices based on oscillating wave voltage, which is aimed to verify performances of oscillating wave voltage generator, partial discharge measurement, and partial discharge localization. The proposed verification system is expected to be applied in the admittance testing and regular verification of oscillating wave test system, for the purpose of improving the its accuracy and standardization in partial discharge measurement of power cable.