Search Results (399)

Gas-insulated switchgear (GIS) offers multiple advantages compared to air-insulated switchgear (AIS); primarily, due to its more compact design and reduced maintenance requirements. In recent years, environmentally friendly replacement gases for SF₆ have become an important research topic, not least because EU regulation will ban the use of SF₆ in new equipment for its member states in the coming years. For detecting defects inside equipment, partial discharge (PD) measurements are an important and well-established method, including in acceptance tests (FAT and SAT) and online monitoring. An important question is whether the PD behavior of various defects analyzed in SF₆ differs in potential replacement gases. In this work, standard geometries in form of needle plane arrangements were used to analyze the PD inception behavior of natural origin gases (synthetic air, CO₂ and N_2,) in comparison to SF₆ at various application relevant pressures. PD was measured both by the conventional (IEC 60270 conform) and UHF technique, recording the phase resolved partial discharge patterns (PRPDs), as well as emitted UHF-pulses. The tip radius and the protrusion length of the needle electrode were varied in order to investigate the influence of the electric field distribution on the PD inception behavior. Results show positive pressure dependence, but also deviations from the linear growth of PDIV, intermittent discharge behavior in synthetic air for some conditions and high-current discharges in the N₂ in the setup used. Full article

The ultra-high frequency (UHF)-based partial discharge (PD) detection technology for gas-insulated switchgear (GIS) has achieved large-scale applications due to its high sensitivity and real-time monitoring capabilities. However, long-term service-induced antenna corrosion in UHF sensors may lead to degraded reception characteristics. To ensure the credibility of monitoring data, on-site sensor calibration under ambient noise conditions is required. This study first analyzes the time–frequency domain characteristics of white noise received by UHF sensors in GIS environments. Leveraging the transceiver reciprocity principle of sensors, a noise reconstruction method based on external sensors is proposed to simulate on-site white noise. Subsequently, CST simulation models are established for both standard and degraded sensors, quantifying the impact of factors like antenna corrosion on performance parameters such as echo impedance S11 and voltage standing wave ratio (VSWR). Finally, the two sensor models are coupled into GIS handholes for comparative simulation analysis. Results show that antenna corrosion causes resonant frequency shifts in sensors, reducing PD signal power by 55.27% and increasing noise power by 64.11%. The signal-to-noise ratio (SNR) decreases from −9.70 dB to −15.34 dB, with evident waveform distortion in the double-exponential PD pulses. These conclusions provide theoretical references for on-site UHF sensor calibration in noisy environments. Full article

Long-term partial discharge (PD) erosion is an important factor causing insulation failure. With the rapid development of power electronics and the widespread application of inverters, more insulation is subjected to repetitive frequency square wave signals. To understand the void PD characteristics of insulation subjected to repetitive frequency square wave signals, a finite element simulation model of void PD is established based on the ABC model. A time-domain waveform and a phase-resolved partial discharge (PRPD) plot of void PD under repetitive frequency square wave with different overshoot rates are simulated. Then, void PD is measured under different overshoot rates and compared with the simulation results. Finally, the influence of overshoot voltage on internal discharge is analyzed. The simulation and experimental results show that the overshoot rate positively correlates with statistical characteristics, such as the average PD number and maximum PD quantity. Void PD events mainly occur in the overshoot portion of the repetitive frequency square wave. Therefore, the overshoot portion of the repetitive frequency square wave is one of the key factors contributing to severe PD in insulation. Full article

Oil–paper insulation in oil-immersed transformers undergoes a concealed degradation process that is difficult to detect during operation. To understand its discharge behavior, this study examines partial discharge characteristics under controlled moisture absorption and thermal aging. Experiments on S-PD (Surface Partial Discharge) and N-PD (Needle Partial Discharge) were carried out, and partial discharge patterns, discharge frequency, and breakdown voltage were collected to analyze discharge evolution. The results show that partial discharge develops through three stages: initiation, development, and pre-breakdown. In the initiation stage, pulses are sparse with low amplitudes and appear near the voltage peak. During development, both amplitude and discharge frequency increase, and the phase range expands. As breakdown approaches, pulse amplitude rises sharply, the phase distribution covers almost the full cycle, and conductive channels begin to form. Skewness, Peak Degree, and Maximum Steepness were extracted from statistical discharge maps to compare moisture-affected and aged samples. The findings provide experimental support for developing state-evolution-based failure warning models and diagnostic criteria, contributing to improved operational safety of oil–paper insulation systems. Full article

High-voltage bushings are critical insulation components, yet conventional PRPD-based severity assessment methods that rely on global pattern morphologies such as “rabbit ears” and “tortoise shell” remain coarse, lack local sensitivity, and fail to track continuous degradation. This paper proposes a dynamic severity assessment method that shifts the focus from global contours to dense partial discharge (PD) clusters, defined as high-density aggregations of PD pulses in specific phase–magnitude regions of PRPD patterns. Each dense cluster is treated as the statistical projection of a physical discharge channel, and the evolution of its number, intensity, location, and shape provides a fine-scale description of defect development. A multi-level relative density and morphological image processing algorithm is used to extract dense clusters directly from PRPD histograms, followed by a 20-dimensional feature set and a five-index system describing discharge activity, development speed, complexity, instability, and evolution trend. A fuzzy comprehensive evaluation model further converts these indices into three severity levels with confidence measures. Long-term degradation tests on defective bushings demonstrate that the proposed method captures key turning points from dispersed multi-cluster patterns to a single dominant cluster and yields a stable, stage-consistent severity evaluation, offering a more sensitive and physically interpretable tool for condition monitoring and early warning of HV bushings. The method achieved a high evaluation confidence (average 60.1%), which rose to 100% at the critical failure stage. It successfully identified three distinct degradation stages (stable, accelerated, and critical) across the 49 test intervals. A quantitative comparison demonstrated significant advantages: 8.3% improvement in early warning (4 windows earlier than IEC 60270), 50.6% higher monotonicity, 125.2% better stability, and 45.9% wider dynamic range, while maintaining physical interpretability and requiring no training data. Full article

Field verification of contact-type ultrasonic sensors enables rapid evaluation of their sensitivity performance, thereby ensuring the accuracy of partial discharge (PD) ultrasonic monitoring results. However, during the verification process, both the standard sensor and the sensor under testing are inevitably affected by ambient noise when receiving verification signals, which can result in significant errors in the verification outcome. To address this issue, a noise suppression method is proposed in this study, which integrates multivariate variational mode decomposition (MVMD) optimized by an improved particle swarm optimization (IPSO) algorithm with a hyperbolic tangent-modulated exponential decay wavelet thresholding technique. First, the IPSO algorithm is employed to automatically optimize the parameters of MVMD. Then, the dominant components of the verification signal are selected based on the energy entropy of each decomposed mode. Subsequently, a novel wavelet threshold function incorporating hyperbolic tangent modulation and exponential decay is constructed and combined with an improved thresholding strategy to denoise the residual noise in the dominant components. Finally, a verification platform based on a real-type transformer is established. Both simulated and measured signals are denoised and subjected to sensitivity verification using the proposed method. Comparative analysis with noise-affected verification results demonstrates that the proposed method effectively suppresses noise in the verification signals and improves the accuracy of the sensitivity verification. Full article

Partial discharge (PD) is not only the primary manifestation of insulation deterioration in gas-insulated switchgear (GIS) but also a critical indicator of the equipment’s insulation condition. PD in GIS typically occurs at media interfaces such as the surface of the basin insulator and is characterized by high randomness and low amplitude. Conventional built-in ultra-high frequency sensors exhibit limitations in early warning and detection performance. This study proposes and demonstrates a self-sensing shielding ring embedded within the basin insulator, functioning as a novel UHF sensor. Finite-difference time-domain (FDTD) is a numerical method used to solve problems involving electromagnetic fields. Based on actual GIS structural parameters, a FDTD simulation platform is constructed and a built-in sensor is used as a control to evaluate the receiving performance of the self-sensing shielding ring for PD signals. Time-domain array simulations are conducted to investigate the influence of radial, angular and axial positions on the observed performance. The results show that the proposed shielding ring exhibits broadband and low-reflection characteristics, achieving an average S₁₁ of −6.347 dB, which is significantly lower than those of the built-in sensors (−1.270 dB and −1.274 dB). The results demonstrate that the self-sensing shielding ring enables high sensitivity and the wideband detection of partial discharge, providing a new design approach and technical foundation for online early-warning systems in GIS. Full article

This paper comprehensively reviews advanced signal processing methods for partial discharge (PD) analysis, covering traditional time-frequency techniques, wavelet transform, Hilbert–Huang transform, and artificial intelligence-based methods. This paper critically examines the principles, advantages, limitations, and applicable scenarios of each method. A key contribution of this review is the systematic comparison of these methods, highlighting their evolution and complementary roles in processing non-stationary and noisy PD signals. However, a significant gap in current research remains the lack of standardized, explainable, and embeddable AI solutions for real-time, fine-grained PD classification. Future trends point to hybrid approaches and edge AI systems that combine physical insights with lightweight deep learning models to improve diagnostic accuracy and deployability. Full article

Recently, partial discharge pattern recognition (PDPR) for transmission cables has garnered increasing attention due to the severe power outages, equipment damage, and even major safety incidents resulting from the failure of partial discharge (PD) detection. However, existing PD data samples usually suffer from highly similar features and unbalanced distribution. Determining how to precisely realize the PDPR has become a challenge. In this study, an effective PDPR approach is proposed based on a newly designed deconstructed PD (DecPD) model and a customized loss function for PDPR. Notably, the refined deep learning network captures the discriminative features in both temporal and spatial dimensions through a dual-channel learning architecture. Additionally, an adaptive focal loss function is designed, which introduces a peak factor to establish focusing parameters for PDPR, thereby addressing the class imbalance issues. A comprehensive experimental evaluation using real datasets generated on a physical platform is conducted to verify our proposed method. Compared to other existing methods, our DecPD approach demonstrates superior performance, achieving an overall accuracy of 96.65% in the presence of environment noise. Full article

The increasing diffusion of high-voltage electrical assets in the field of aviation and aerospace sectors, due to the transition towards electrified transportation, brings significant challenges related to electrical insulation that need to be addressed. This work proposes a procedure to obtain reliable and partial discharge-free designs of aviation/aerospace electrical and electronic components, which stem from the recently developed three-leg approach. A partial discharge (PD) inception model that contains an explicit dependence on pressure is proposed and validated through a wide range of pressure levels ranging from 0.05 to 3 bar in air and CO₂. Model fitting to measured partial discharge inception voltage (PDIV) values appears to be very good in air as well as CO₂ environments; therefore, it can be speculated that the proposed approach can be used to predict PDIV in the case of solid insulation systems at different operating pressures, enabling PD-free insulation system designs to be carried out. Full article

High-voltage (HV) cable systems—particularly those insulated with cross-linked polyethylene (XLPE)—are increasingly deployed in both AC and DC applications due to their excellent electrical and mechanical performance. However, their long-term reliability is challenged by partial discharges (PD), insulation aging, space charge accumulation, and thermal and electrical stresses. This review provides a comprehensive survey of the state-of-the-art technologies and methodologies across several domains critical to the assessment and enhancement of cable reliability. It covers advanced condition monitoring (CM) techniques, including sensor-based PD detection, signal acquisition, and denoising methods. Aging mechanisms under various stressors and lifetime estimation approaches are analyzed, along with fault detection and localization strategies using time-domain, frequency-domain, and hybrid methods. Physics-based and data-driven models for PD behavior and space charge dynamics are discussed, particularly under DC conditions. The article also reviews the application of numerical tools such as FEM for thermal and field stress analysis. A dedicated focus is given to machine learning (ML) and deep learning (DL) models for fault classification and predictive maintenance. Furthermore, standards, testing protocols, and practical issues in sensor deployment and calibration are summarized. The review concludes by evaluating intelligent maintenance approaches—including condition-based and predictive strategies—framed within real-world asset management contexts. The paper aims to bridge theoretical developments with field-level implementation challenges, offering a roadmap for future research and practical deployment in resilient and smart power grids. This review highlights a clear gap in fully integrated AC/DC diagnostic and aging analyses for XLPE cables. We emphasize the need for unified physics-based and ML-driven frameworks to address HVDC space-charge effects and multi-stress degradation. These insights provide concise guidance for advancing reliable and scalable cable assessment. Full article

High-frequency current detection of partial discharge (PD) at transformers on-site faces complex noise interference, which severely impacts the accuracy of PD detection. To address this issue, an intelligent interference suppression algorithm for PD signals based on adaptive waveform feature recognition is proposed. First, a 10 MHz high-pass filter is applied to eliminate the influence of periodic narrowband interference on the zero-crossing count of the time-series. Non-pulse noise is removed based on the instantaneous zero-crossing density of the signal. Next, the start and end times of each pulse are determined, and the corresponding waveform segments are extracted from the original signal to form a pulse array. Subsequently, waveform features of the pulses are extracted, and discrimination thresholds for the feature parameters are calculated based on univariate analysis. Finally, each pulse is adaptively identified based on its waveform features, and PD signals are screened out. The proposed algorithm was tested using PD signals superimposed with on-site noise as well as field-measured signals. The results demonstrate that the algorithm can intelligently identify PD signals and significantly reduce PD signal attenuation, exhibiting excellent suppression effects on complex noise interference in on-site PD detection at transformers. Full article

Rapid evolution in electrified transportation and, in general, sustainability of electrical and electronic assets is turning the traditional power supply and utilization into something more complex and less known. This transition involves increasing operating voltage and specific power, as well as various types of power supply sources, from AC sinusoidal to DC and power electronics. This revolution, beneficial for asset efficiency and resilience, does come at the cost of increased risk of failure for electrical insulation systems. Intrinsic and extrinsic aging mechanisms are not completely known under DC and power electronics, and the risk of inception of partial discharges, PD, which is the most harmful extrinsic aging factor for electrical insulation, is as high, or even higher, compared with AC. To complicate the picture, electrical and electronic components can be used at different pressure levels, such as in aerospace, and it is known that partial discharge inception voltage, PDIV, drops down, and PD magnitude increases, lowering pressure. Models to predict PDIV for surface and internal discharges, as function of pressure, have been proposed recently, but they cannot be applied straightforwardly on practical asset components where type and locations of defects generating PD is unknown. This paper wants to close this application gap. Derivation and validation of an approximate, heuristic model able to predict PDIV at various pressure levels below and above the standard atmospheric pressure, SAP, are dealt with in this paper, referring to typical asset components such as cables, motors, printed circuit-boards, PCB, and under sinusoidal AC voltage. The good capability of the model to predict PDIV and any investigated pressure, from 3 to 0.05 bar, is validated by PD measurements performed using an innovative, automatic PD analytics software able to identify the typology of defect generating PD, i.e., whether surface or internal. Full article

Under operating conditions, metallic particle contaminants inside Gas-Insulated Switchgears (GIS) represent a major threat that can initiate partial discharges (PD) and lead to insulation failure. To investigate the discharge patterns under combined AC and switching impulse voltages, this paper presents an experimental study conducted in SF6 gas on wire-shaped, spherical, and Mixed Metal Particles. By synchronously analyzing PD time-domain waveforms, Phase-Resolved Partial Discharge (PRPD) patterns, and high-speed motion camera recordings, the correlation between particle motion behavior and discharge signals was systematically examined. The results indicate that wire particles exhibit a significant discharge initiation delay under the combined voltage; however, intense, discrete discharges with large magnitudes occur during their vertical jumping phase. In contrast, spherical particles can be activated within the first power frequency cycle without delay, but the subsequent discharge magnitudes are limited. The characteristics of hybrid particles lie between these two types, demonstrating a staged evolution described as “spherical particles lead initiation, wire particles dominate discharge.” Furthermore, under the sustained AC voltage, hybrid particles trigger a more dispersed and violent discharge process. These findings reveal the complex motion-discharge mechanism of Mixed Metal Particles, providing critical insights for fault mechanism analysis and insulation protection related to particle contamination in practical GIS equipment. Full article

Power electronic devices for AC/DC and AC/AC conversion are, nowadays, widely distributed in electrified transportation and industrial applications, which can determine significant deviation in supply voltage waveform from the AC sinusoidal and promote insulation extrinsic aging mechanisms as partial discharges (PDs). PDs are one of the most harmful processes as they are able to cause accelerated extrinsic aging of electrical insulation systems and are the cause of premature failure in electrical asset components. PD phenomenology under pulse width modulated (PWM) voltage waveforms has been dealt with in recent years, also through some IEC/IEEE standards, but less work has been performed on PD harmfulness under AC distorted waveforms containing voltage harmonics and notches. On the other hand, these voltage waveforms can often be present in electrical assets containing conventional loads and power electronics loads/drives, such as for ships or industrial installations. The purpose of this paper is to provide a contribution to this lack of knowledge, focusing on PD sensing and phenomenology. It has been shown that PD patterns can change considerably with respect to those known under sinusoidal AC when harmonic voltages and/or notches are present in the supply waveform. This can impact PD typology identification, which is based on features related to PD pattern-based physics. The adaptation of identification AI algorithms used for AC sinusoidal voltage as well as distorted AC waveforms is discussed in this paper, showing that effective identification of the type of defects generating PD, and thus of their harmfulness, can still be achieved. Full article