Search Results (376)

Defects in GIS can be effectively detected by detecting the partial discharge (PD). The common methods of detecting partial discharge are pulse current, ultrasonic and UHF (ultra-high frequency). However, the results of different methods may be different due to the different physical quantities detected. It is important to research the differences between the PD detection methods for the PD detection and analysis. In this study, we designed metal protrusion defects in GIS, including protrusion on the conductor and enclosure. Then, we detected the PD of defects using pulse current, UHF and ultrasonic methods at the same time. The PRPD patterns, maximum discharge amplitude of different defects and PD inception voltage (PDIV) detected by the three methods were analyzed. The PRPD patterns and discharge amplitude of the different methods were very similar to each other, but the PDIVs were different. It can be concluded that the process from the PD inception to breakdown can be divided into four sections based on the PRPD and the maximum discharge amplitude. The similarity between the three methods is because their signals are all related to the pulse current during the PD process, and differences in their PDIVs are caused by the differences in sensitivity. The sensitivity of the pulse current is the lowest among the three methods due to its poor anti-jamming capability. The sensitivity of UHF is higher, and that of ultrasonic is the highest. Full article

Partial discharge (PD) in insulators will not only lead to the gradual degradation of insulation performance but even cause power system failure in serious cases. Because there is strong noise interference in the field, it is difficult to accurately locate the position of the PD source. Therefore, this paper proposes a three-dimensional spatial localization method of the PD source with a four-element ultra-high-frequency (UHF) array based on improved wavelet packet dynamic threshold denoising and the $G_{xx}-\beta$ generalized cross-correlation algorithm. Firstly, considering the field noise interference, the PD signal is decomposed into sub-signals with different frequency bands by the wavelet packet, and the corresponding wavelet packet coefficients are extracted. By using the improved threshold function to process the wavelet packet coefficients, the PD signal with low distortion rate and high signal-to-noise ratio (SNR) is reconstructed. Secondly, in order to solve the problem that the amplitude of the first wave of the PD signal is small and the SNR is low, an improved weighting function, $G_{xx}-\beta$, is proposed, which is based on the self-power spectral density of the signal and is adjusted by introducing an exponential factor to improve the accuracy of the first wave arrival time and time difference calculation. Finally, the influence of different sensor array shapes and PD source positions on the localization results is analyzed, and a reasonable arrangement scheme is found. In order to verify the performance of the proposed method, simulation and experimental analysis are carried out. The results show that the improved wavelet packet denoising algorithm can effectively realize the separation of PD signal and noise and improve the SNR of the localization signal with low distortion rate. The improved $G_{xx}-\beta$ weighting function significantly improves the estimation accuracy of the time difference between UHF sensors. With the sensor array designed in this paper, the relative localization error is 3.46%, and the absolute error is within 6 cm, which meets the requirements of engineering applications. Full article

This study proposes a novel air gap structure to enhance the sensitivity and saturation resistance of high-frequency current transformers (HFCTs) used in partial discharge (PD) detection for high-voltage equipment. While previous research has shown that air gaps can prevent core saturation, the impact of asymmetrical versus symmetrical air gaps has not been systematically analyzed. In this paper, we perform a detailed simulation-based comparison using Material 43 and Material 78 ferrite cores. The results show that asymmetrical air gaps significantly increase flux density and improve sensitivity compared with symmetrical designs, achieving a flux enhancement of up to 40%. A physical mechanism based on flux concentration and reduced fringing effects is proposed to explain these improvements. This study provides a new design strategy for HFCTs, enhancing their performance under high-current conditions and improving the reliability of online PD monitoring systems. Future work will involve experimental validation to further confirm these findings. Full article

In the field of partial discharge (PD) analysis, traditional methods typically employ single-source PD signal-processing techniques. However, these approaches exhibit significant limitations when applied to transformers with relatively complex structures. To overcome these limitations and achieve precise characterization of composite PD signatures, this study proposes an improved power spectrum segmentation method (IPSK) based on spectral kurtosis. Firstly, normalized power spectral kurtosis is used to select the appropriate parameters. Then, through the improved power spectrum segmentation method, the segmentation frequency band with the least noise is obtained. Finally, the instantaneous signal components with physical significance are obtained by reconstructing each frequency band through inverse fast Fourier transform. By analyzing the simulated signals and measured data of partial discharge, the proposed method is compared with EWT, AEFD, VMD, and CEEMDAN. The results show that IPSK has a good suppression effect on noise interference. Full article

Deep learning-based models have achieved considerable success in partial discharge (PD) fault diagnosis for power systems, enhancing grid asset safety and improving reliability. However, traditional approaches often rely on centralized training, which demands significant resources and fails to account for the impact of noisy operating conditions on Intelligent Electronic Devices (IEDs). In a gas-insulated switchgear (GIS), PD measurement data collected in noisy environments exhibit diverse feature distributions and a wide range of class representations, posing significant challenges for trained models under complex conditions. To address these challenges, we propose a Self-Supervised Asynchronous Federated Learning (SSAFL) approach for PD diagnosis in noisy IED environments. The proposed technique integrates asynchronous federated learning with self-supervised learning, enabling IEDs to learn robust pattern representations while preserving local data privacy and mitigating the effects of resource heterogeneity among IEDs. Experimental results demonstrate that the proposed SSAFL framework achieves overall accuracies of 98% and 95% on the training and testing datasets, respectively. Additionally, for the floating class in IED 1, SSAFL improves the F1-score by 5% compared to Self-Supervised Federated Learning (SSFL). These results indicate that the proposed SSAFL method offers greater adaptability to real-world scenarios. In particular, it effectively addresses the scarcity of labeled data, ensures data privacy, and efficiently utilizes heterogeneous local resources. Full article

AC power cables play an important role in power systems, in the transmission and distribution of electrical energy. For this reason, to ensure high operational reliability, voltage withstand tests and diagnostic tests are performed at every stage of their technical life to determine the condition of cable insulation. Due to the large electrical capacitances of cable systems, modern testing methods use very low frequency (VLF) and damped oscillating (DAC) voltages. The research presented in the article analyzed the effect of the test voltage waveform parameters on the partial discharge (PD) inception conditions in spherical gaseous voids present in the XLPE insulation of AC cable model. Using COMSOL 6.1 and MATLAB R2021b, a coupled electro-thermal model of a 110 kV AC cable was implemented, for which the critical gaseous void dimensions were estimated and phase-resolved PD patterns were generated for the rated voltage and the VLF and DAC test voltages specified in the relevant standards. In the analyses for the rated voltage, the influence of internal temperature distribution, which causes modification of XLPE permittivity, was taken into account in the numerical cable model. Full article

The growing complexity of electrical infrastructure has elevated partial discharge (PD) detection to a crucial methodology for ensuring power system safety. Current PD pattern recognition approaches encounter persistent challenges in low-data scenarios, particularly regarding classification accuracy and model generalizability. This study develops a Firefly Algorithm with a Black Hole Mechanism-ResNet18 (FBH-ResNet18) framework that synergistically integrates the Firefly Algorithm with the Black Hole Mechanism (FBH algorithm) optimization with residual neural networks for PD signal classification using phase-resolved partial discharge (PRPD) mappings. A dedicated experimental platform first acquires PD signals through UHF sensors, which are subsequently converted into two-dimensional PRPD representations. The FBH algorithm systematically optimizes four key hyperparameters within the ResNet18 architecture during network training. The Black Hole Mechanism and improved population dynamics enhance optimization efficiency, resulting in more accurate hyperparameter tuning and improved model performance. Comparative evaluations demonstrate the enhanced performance of this parameter-optimized model against alternative configurations. Experimental results indicate that the improved ResNet18 achieves fast convergence and strong generalization on small-sample datasets, significantly enhancing recognition accuracy. During the first 80 generations of training, the classification accuracy reaches 89.11%, and in the final iteration, the model’s recognition accuracy increases to 92.55%, outperforming other models with accuracies generally below 90%. Additionally, the model shows excellent performance on the test set, with a loss function value of 0.250785, significantly lower than that of other models, indicating superior performance on small sample datasets. This research provides an effective solution for power cable fault diagnosis, offering high practical value. Full article

This study investigates the degradation mechanisms of fiber-reinforced polymer (FRP) matrices in composite insulators under partial discharge (PD) conditions. The degradation products may further cause deterioration of the electrical and chemical resistance (ECR) glass fibers. Using pyrolysis–gas chromatography-mass spectrometry (PY-GC-MS) and high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS-MS), the thermal degradation gas and liquid products of the degraded FRP matrix were analyzed, revealing the presence of organic acids. These acids form when the epoxy resin’s cross-linked bonds break at high temperatures, generating anhydrides that hydrolyze into carboxylic acids in the presence of moisture. The hydrolyzation process is accelerated by hydroxyl radicals produced during PD. The resulting carboxylic acids deteriorate the glass fibers within the FRP matrix by degrading surface coupling agents and reacting with the alkali metal–silica network, leading to the substitution and precipitation of metal ions. Organic acids, particularly carboxylic acids, were found to have a more severe deteriorating effect on glass fibers compared to inorganic acids, with high temperatures exacerbating this process. These findings provide critical insights into the deterioration mechanisms of FRP under operational conditions, offering valuable guidance for optimizing manufacturing processes and enhancing the longevity of composite insulators. Full article

Aging underground cables pose a threatening issue in distribution systems. Replacing all cables at once is economically unfeasible, making it crucial to prioritize replacements. Traditionally, age-based strategies have been used, but they are likely to fail to depict the real condition of cables. Insulation faults are influenced by electrical, mechanical, thermal, and chemical stresses, and partial discharges (PDs) often serve as early indicators and accelerators of insulation aging. The trends in PD activity provide valuable information about insulation condition, although they do not directly reveal the cable’s real age. Due to the absence of an established ranking methodology for such condition-based data, this paper proposes a fuzzy logic and fuzzy analytic hierarchy process (FAHP)-based cable vulnerability ranking framework that effectively manages uncertainty and expert-based conditions. The proposed framework requires only basic and readily accessible data inputs, specifically cable age, which utilities commonly maintain, and PD measurements, such as peak values and event counts, which can be acquired through cost-effective, noninvasive sensing methods. To systematically evaluate the method’s performance and robustness, particularly given the inherent uncertainties in cable age and PD characteristics, this study employs Monte Carlo simulations coupled with a Spearman correlation analysis. The effectiveness of the developed framework is demonstrated using real operational cable data from a Danish distribution network, meteorological information from the Danish Meteorological Institute (DMI), and synthetically generated PD data. The results confirm that the FAHP-based ranking approach delivers robust and consistent outcomes under uncertainty, thereby supporting utilities in making more informed and economical maintenance decisions. Full article

This paper remedies the lack of comparison between studies specifically addressing partial discharge (PD) localization using electrical techniques. It identifies all the elements in need in each technique as well as the equations leading to a precise determination of the discharge site in a cable with a certain length and documents several circuit models set to simulate various types of PD. From the details in this paper, different detection methods can be combined based on the specific requirements of each method for detecting PD. This work thoroughly evaluates several electrical PD detection approaches, including time-based, frequency band, and electromagnetic time reversal (EMTR). Additionally, it gathers circuit modeling for various types of PD along cables to improve detection accuracy. It is evident that all time-dependent methods, despite their simplicity and requiring only a small number of components, face challenges when applied to long cables. This is primarily due to their reliance on signal propagation time. The authors provide profound insights into suggestions for future study areas. This review will provide essential insights and serve as a foundation for researchers to develop more effective methods for detecting PD in cables. Full article

In light of the large and fast-growing use of power electronics in electrical generation, distribution and utilization systems, and with the focus on electrified transportation, evaluating the significance of testing insulation systems for design and quality control under AC sinusoidal or power electronics waveforms is a due knowledge step. This paper has a twofold aim. One is presenting a procedure for the comparison between two insulation system solutions for partial discharge, PD, free design, referring to motorettes of a MV speed-controlled motor. The other is to carry out an evaluation of the most effective testing waveform, from AC sinusoidal to AC modulated (PWM), varying the number of inverter levels and switching the slew rate. It is shown that AC sinusoidal is effective for a qualitative evaluation of insulation system design as regards partial discharge risk, but PD inception voltage can be significantly dependent on supply voltage waveforms. Hence, if quantitative estimation of partial discharge inception voltage is requested, for design and quality control purposes, PWM waveforms as close as possible to those planned under operation should be used. Full article

Partial discharge (PD) analysis is critical for diagnosing insulation degradation in high-voltage equipment. While conventional methods struggle with multi-source PD classification due to signal overlap and noise, this study proposes a hybrid approach combining five time–frequency analysis (TFA) techniques with deep learning (GoogLeNet for simulation, ResNet50 for experiments). PD data are generated through Finite Element Method (FEM) simulations and validated via laboratory experiments. The Scatter Wavelet Transform (SWT) achieves 96.67% accuracy (F1-score: 0.967) in simulation and perfect 100% accuracy (F1-score: 1.000) in experiments, outperforming other TFAs like HHT (70.00% experimental accuracy). The Wigner–Ville Distribution (WVD) also shows strong experimental performance (94.74% accuracy, AUC: 0.947), though its computational complexity limits real-time use. These results demonstrate the SWT’s superiority in handling real-world noise and multi-source PD signals, providing a robust framework for insulation diagnostics in power systems. Full article

Fault diagnosis based on the partial discharge (PD) recognition has been widely applied on a gas-insulated line breaker (GILB) and gas-insulated switchgear (GIS) as a reliable online condition monitoring method. This paper dealt with insulation defect diagnosis based on a Random Forest (RF) algorithm with an optimized feature selection method. Four different types of insulation defect models, such as the free-moving particle (FMP) defect, the protrusion-on-conductor (POC) defect, the protrusion-on-enclosure (POE) defect, and the delamination defect, were prepared to simulate representative PD single pulses and PRPD patterns generated from the GILB. The PD signals generated from defect models were detected using the PRPD sensor which can detect phase-synchronized PD signals with the applied high-voltage (HV) signals without the need for additional equipment. Various statistical PD features were extracted from PD single pulses and PRPD patterns according to four kinds of PD defect models, and optimized features were selected with respect to variance importance analysis. Two kinds of PD datasets were established using all statistical features and top-ranked features. From the experimental results, the RF algorithm achieved accuracy rates exceeding 92%, and the PD datasets using only half of the statistical PD features could reduce the computational times while maintaining the accuracy rates. Full article

The online monitoring of partial discharge (PD) in converter transformers faces significant noise interference. Among these, the periodic pulsating interference caused by the switching process of thyristors is particularly challenging to directly identify from waveforms or frequency spectra, which affects the accuracy of PD monitoring. To address this, a time-domain multi-feature joint recognition algorithm based on periodic consistency scoring and waveform characteristics is proposed. Firstly, the timestamps of each pulse in the signal sequence are extracted based on the cumulative energy function. Secondly, the periodic consistency score and time–frequency feature parameters of each pulse are extracted separately, and an optimal recognition vector is constructed based on univariate screening and redundancy feature checking. Finally, dimensionality reduction is performed using principal component analysis, and the PD signals are separated from periodic pulse interference through fuzzy C-means clustering. The proposed algorithm is applied to denoise PD signals superimposed with on-site noise interference and field-measured signals. The results demonstrate that the algorithm effectively suppresses periodic pulsation interference while significantly reducing the attenuation of PD signals during the denoising process. Full article

This paper introduces a novel diagnostic approach called partial-discharge-activated impulse frequency response analysis (PD-IFRA), developed to overcome the limitations of conventional frequency response analysis (FRA) in detecting partial discharges (PDs) in power transformers. While traditional FRA with low-impulse-voltage excitation (LIVE) effectively identifies mechanical deformations, inter-turn shorts, and insulation faults, it fails to detect incipient PD activity since PD phenomena require excitation beyond the PD inception voltage (PDIV) to initiate. This study proposes, for the first time, the extension of IFRA to moderate impulse voltage levels—without exceeding insulation limits—enabling the early and non-destructive detection of PDs. Experimental validation on a 315 kVA, 11 kV/420 V Dyn11 transformer shows that PD-IFRA effectively identifies PD-related impedance deviations within the 10 kHz–2 MHz frequency range, especially for PD sources associated with floating metal parts. Furthermore, the comparative analysis between normal, short-circuited, and PD-induced conditions demonstrates that PD-IFRA significantly enhances the precursory diagnosis of PDs where conventional FRA fails. This contribution advances transformer condition assessment by integrating PD sensitivity into FRA-based methods without compromising equipment safety. Full article