Search Results (129)

The Extrinsic Fiber Fabry–Perot Interferometer (EFPI) fiberoptic ultrasonic sensor can be used to detect partial discharge ultrasonic signals inside gas-insulated switchgear (GIS) and has various uses in pattern recognition research. Compared with traditional piezoelectric sensors, it offers both high sensitivity and strong resistance to interference. Based on this information, we construct four typical PD models (representing the tip, metal particle, suspension, and surface) in a GIS cavity filled with 0.4 MPa SF₆ gas, 0.6 MPa SF₆N₂ gas, and 0.5 MPa C₄F₇NCO₂ gas. We then use the EFPI sensor to detect PD ultrasonic signals, extract their waveform characteristics to form a database of characteristic parameters, and apply the Transformer algorithm. The detected signal offers outstanding pattern recognition when applied to GIS discharge samples in the laboratory, and the Transformer algorithm achieves a 100% recognition success rate, which is much higher than that of Support Vector Machine (SVM) machine learning algorithms. Full article

To address the problems of closed-loop task organization, strong corridor constraints, and path failure after local disturbances in unmanned aerial vehicle (UAV) inspection of gas-insulated switchgear (GIS) rooms, this paper proposes a topology-and-corridor-guided bias-suppressed D* (TCG-BS-D*) method for closed-loop three-dimensional (3D) path planning and local replanning. The proposed method constructs a structured guidance model based on the inspection-corridor topology, generates local 3D path segments according to a predetermined inspection sequence, and forms a nominal closed-loop inspection path through bias suppression and path regularization. Meanwhile, for local maintenance blockage and dynamic disturbance scenarios, an alternative local replanning strategy is applied to the affected path segments. Simulation results show that, under the static closed-loop inspection condition, the proposed method achieves a total path length of 700.22 m, a total inspection time of 269.32 s, an average safety clearance of 8.18 m, 37 large-angle turns, a corridor adherence rate of 80.73%, and a task completion rate of 100%, showing superior performance in inspection efficiency, safety margin, trajectory regularity, and corridor consistency. Under the local blockage condition, the replanned path introduces path-length and time increments of 71.29 m and 25.88 s, respectively, while maintaining the minimum safety clearance at 1.52 m and increasing the corridor adherence rate to 83.91%. Under dynamic disturbance conditions, the minimum dynamic safety clearance is improved from −2.71 m to 17.84 m, effectively eliminating the local dynamic collision risk. The results demonstrate that the proposed method can balance closed-loop path-generation efficiency, corridor-structure consistency, safety margin, and adaptability to local disturbances, providing an effective solution for UAV inspection path planning in GIS rooms. Full article

With the ongoing trend of miniaturization and intelligent power transmission equipment, the compact design of environmentally friendly gas-insulated switchgear (GIS) has emerged as a critical technical challenge. This study presents a detailed case study of a 40.5 kV dry air-insulated switchgear under specific dimensional constraints. Specifically, the cabinet width was reduced from 1000 mm to 800 mm, significantly narrowing the phase-to-phase and phase-to-ground clearances. A high-fidelity three-dimensional electric field model was established using the finite element method to evaluate the dielectric stress distribution within the enclosure. Numerical results indicate pronounced electric field concentrations at critical regions—including copper busbar joints, disconnector contacts, and the inlet bushing shielding rings—where local intensities exceeded the insulation safety threshold. To mitigate these issues, integrated design refinement strategies were evaluated, encompassing the structural modification of shielding rings, the application of silicone rubber coatings, and insulation reinforcement via heat-shrinkable tubing. Comparative analysis and experimental results demonstrate that the refined configuration effectively suppressed the peak electric field intensity. Finally, the design was validated through comprehensive dielectric tests, including a 215 kV lightning impulse withstand voltage test. This work may offer useful engineering references and quantitative data for the ultra-compact design of eco-friendly switchgear under similar constraints. Full article

Partial discharge (PD) signals acquired from gas-insulated switchgear (GIS) are often severely contaminated by discrete-spectrum interference and periodic narrowband noise, which impairs the accuracy of subsequent fault diagnosis. This paper proposes a hybrid denoising method that integrates Spearman coefficient-optimized variational mode decomposition (S_VMD), spatially related recursive sample entropy (Sdr_SampEn) for intrinsic mode function (IMF) classification, an improved wavelet threshold function, and Savitzky–Golay (SG) filtering. First, the Spearman correlation coefficient between the original signal and the reconstructed signal is used to adaptively determine the optimal mode number K of VMD, avoiding the over- and under-decomposition problems of conventional VMD. Second, Sdr_SampEn, which characterizes signal irregularity along both the Chebyshev distance and spatial direction of a recurrence plot, is employed to classify the obtained IMFs into noise-dominant and PD-dominant components, with the discrimination threshold calibrated as p = 1.94 at 0 dB. Third, an improved wavelet threshold function—continuous at the threshold and asymptotically unbiased—is applied to the noise-dominant components, while SG filtering is applied to the PD-dominant components, after which the denoised signal is reconstructed. The results demonstrate that the proposed method effectively suppresses both white and narrowband noise while preserving the detailed morphology of PD pulses. Full article

With the rapid construction of new power systems characterized by high renewable energy penetration, high power electronics integration, and high voltage levels, the insulation reliability of critical power equipment—including cable accessories, gas-insulated switchgear (GIS), and power electronic modules—faces unprecedented challenges. Field grading materials (FGM), as core functional media for adaptive electric field homogenization and insulation failure prevention, have emerged as a research hotspot spanning materials science, electrical engineering, and polymer engineering. Starting from the current research status of FGM, this review systematically summarizes filler optimization strategies, covering single fillers, hybrid fillers, trace co-fillers, and structural modification approaches. The applications of FGM in transmission cables, GIS, high-voltage electrical machines, and wide-bandgap power electronic modules are then elaborated in detail. Emphasis is placed on performance enhancement routes of FGM, particularly thermal conductivity improvement via constructing three-dimensional thermally conductive networks and intelligent early warning based on thermochromic materials. Finally, the existing bottlenecks of FGM are analyzed in terms of material stability, multi-physical field coupling adaptation, and engineering industrialization. Future development trends are prospected toward high-performance, multifunctional, intelligent, and engineering-oriented FGM. This review aims to provide theoretical references and technical support for the design and application of advanced FGM in new power systems. Full article

To address the difficulty of directly measuring the internal conductor temperature and the complex influence of external environmental factors on gas-insulated switchgear (GIS), a three-dimensional thermal–fluid multiphysics coupling model was developed for a 110 kV three-phase common-enclosure GIS disconnect switch. The model incorporates contact resistance heating, natural convection of SF₆ gas, wind speed, and solar radiation. The effects of contact resistance and environmental factors on the temperature field distribution were systematically investigated. The results show that an increase in contact resistance significantly raises the conductor temperature, while higher wind speeds effectively reduce the temperature rise of the equipment. Solar radiation substantially increases the enclosure temperature, whereas ambient temperature has little influence on temperature rise. Based on the enclosure temperature rise, a conductor temperature-rise prediction model and a multi-factor correction model were established. Validation results indicate that all models achieved coefficients of determination greater than 0.98, with prediction errors controlled within ±2 °C. The proposed method enables the accurate prediction of conductor temperature under complex environmental conditions and provides technical support for condition monitoring and overheating fault diagnosis of GIS equipment. Full article

The reliable operation of Gas-Insulated Switchgear (GIS) is crucial for power system safety, yet automatic foreign object inspection within its cavities remains challenging due to low-light conditions and strong reflections. This paper proposes a multi-modal few-shot learning framework for high-precision foreign object segmentation in GIS. To overcome imaging interference, we first establish a dual-light (visible and ultraviolet) image acquisition system and design a lightweight fusion network to adaptively integrate multi-modal features, enhancing scene representation. For the core few-shot segmentation task, we introduce a novel Multi-Similarity Guided Branch Network (MSBNet). This network employs a support-query dual-branch architecture to extract sample prototypes. It features an improved background similarity guidance mechanism to suppress base-class feature interference and a multi-similarity fusion module that synergistically integrates multi-level and multi-metric information, which significantly improves the continuity and boundary accuracy of the segmentation masks. Experiments on our GIS dataset demonstrate that, under extremely limited sample conditions, the proposed method rapidly adapts to unseen foreign object classes and substantially outperforms existing few-shot segmentation baselines. Full article

The operational reliability of gas-insulated switchgear/gas-insulated transmission lines (GIS/GIL) is critically threatened by internal metallic particles, which serve as primary triggers for insulation degradation. Conventional partial discharge (PD) detection methods often lack sensitivity during the early stages of particle movement. To overcome these limitations, this study aims to develop a novel non-intrusive defect diagnosis methodology based on the analysis of mechanical vibration signals. The coupled particle motion model integrating the electrostatic field, particle tracking, and multibody dynamics has been established. This model reveals the dynamic law that metallic particles migrate toward the conductor and undergo charge polarity reversal after collision, with a maximum speed of 2.7 m/s. Meanwhile, the peak vibration acceleration excited by the collision is calculated as 0.02 m/s². Accordingly, the high-voltage experimental platform with the full-scale prototype is built to simulate the actual operating conditions of the power grid. With the particle defects set inside the prototype, vibration signals are collected by using an accelerometer, and the measured peak vibration acceleration is 0.017 m/s². Finally, a defect diagnosis method based on the Hilbert–Huang Transform (HHT) and correlation coefficient analysis is proposed. This method uses Empirical Mode Decomposition (EMD) to extract the IMF4 component of the signal in the vicinity of the 1000 Hz frequency band. When particle defects occur, the correlation coefficient between the IMF4 component and the original signal exceeds 0.7668. This vibration-based monitoring technique provides an alternative for the condition-based maintenance of GIS/GIL, offering significant engineering value for enhancing the safety and reliability of power transmission infrastructure. Full article

To address the challenges of extracting mechanical fault features in Gas Insulated Switchgear (GIS) under complex operating conditions and the insufficient diagnostic accuracy associated with traditional one-dimensional time-series signals, this paper proposes a GIS fault-classification method based on composite dimensional upscaling images of vibration signals and the Vision Transformer (ViT) algorithm. This method first employs a sliding window slicing strategy to segment the raw long-sequence vibration signals into multiple overlapping time segments. Then, it utilizes the Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), and Markov Transition Field (MTF) to perform composite dimensional upscaling on these segmented signals, projecting the resulting features into a three-channel RGB composite two-dimensional image. Subsequently, the global self-attention mechanism of the Vision Transformer (ViT) processes the dimensionally upscaled data to achieve the fault classification of the GIS equipment. Experimental results demonstrate that, compared to single-channel ViT variants, Convolutional Neural Networks (CNN), and Residual Networks (ResNet), the proposed algorithm achieves the highest overall performance in the training set experiments, and the superiority of this method is verified through ablation studies and comparative experiments. Furthermore, the average accuracy of the algorithm on the testing set reaches 95.63%, proving the reliability and accuracy of the proposed method. Full article

The residual sub-millimeter metal particles in gas-insulated metal enclosed switchgear (GIS) and gas-insulated transmission lines (GILs) are significant factors that trigger insulation failures. During actual operation, these particles not only endure the action of alternating electric fields but also are continuously stimulated by mechanical vibrations. Current research mostly focuses on the behavior of millimeter-sized particles under a single physical field, lacking in-depth understanding of the jumping characteristics of sub-millimeter-scale particles under the combined action of alternating electric fields and mechanical vibrations. This paper has built a collaborative action test platform and constructed a spherical-bowl-shaped electrode defect model. It systematically studied the jumping behavior, motion evolution, and local discharge characteristics of 20-mesh and 40-mesh irregular aluminum particles under the combined action of different voltages (0–7 kV) and mechanical vibrations (amplitude 0.01–0.1 mm, frequency 10–100 Hz). The results show that mechanical vibrations provide initial kinetic energy for the particles, significantly reducing the threshold for jumping, and are the key initiating factor in the collaborative action; in the low-voltage stage, vibration dominates the jumping behavior, while in the high-voltage stage, the electric field dominates the motion evolution; and under dual stimulation, the jumping area of the particles is wider and the motion forms are more diverse (such as flying-flying motion, vertical state, pile-up excitation, etc.), and the starting voltage of discharge is significantly reduced, the discharge repetition rate increases with the increase in vibration intensity and voltage, and is closely related to the particle size. This paper reveals the uniqueness of particle motion and discharge under the collaborative action, providing a theoretical basis for the assessment of multi-physical field states and fault prediction of GIS/GIL. Full article

To address the inverse identification of contact-related thermal faults in gas-insulated switchgear (GIS), this study proposes a method for contact resistance inversion and internal temperature field reconstruction. The proposed method enables the estimation of faulty internal contact resistance using external enclosure temperature data, while simultaneously reconstructing the internal temperature field. First, a forward numerical model of GIS is established, and a POD-Kriging surrogate model is developed to achieve second-level rapid prediction of the forward problem. Based on this surrogate model, the thermal fault inversion problem is formulated as an optimization problem of fault parameters and solved using the Grey Wolf Optimizer. GIS temperature-rise experiments are performed to validate the numerical model, and a real GIS contact fault case is further analyzed. The results indicate that the proposed method yields an average inversion error of 9.5% for degraded contact resistance, with the maximum error at internal temperature monitoring points remaining below 8%. The total inversion time is approximately 30 s. These findings demonstrate that the proposed method is capable of effective online inversion and diagnosis of contact-related thermal faults in GIS equipment. Full article

The detection of sulfur hexafluoride (SF₆) decomposition products is critical for diagnosing insulation faults in gas-insulated switchgear (GIS). In this study, a vanadium-doping strategy was incorporated into the graphene/MoS₂ (GM) heterojunction to design a vanadium-doped graphene/MoS₂ (GMV) heterojunction material. Leveraging first-principles density functional theory (DFT), the adsorption behaviors of five characteristic SF₆ and its decomposition gases (H₂S, SO₂, SOF₂, SO₂F₂) on intrinsic GM and GMV were systematically investigated to evaluate their potential for gas sensing applications. Computational results reveal that intrinsic GM exhibits only weak physical adsorption toward all target molecules, with low adsorption energies and negligible charge transfer, which fails to meet practical application requirements. In contrast, GMV demonstrates significantly enhanced adsorption energies for H₂S, SO₂, and SOF₂ at vanadium sites (with a maximum value of −0.388 eV for SO₂) and shorter adsorption distances, while SO₂F₂ and SF₆ preferentially adsorb near electron-deficient carbon regions. Intrinsic GMV displays semimetallic properties, with a Fermi level at 0.126 eV and a band gap of 0.0017 eV. Upon adsorption of H₂S, SOF₂, SO₂F₂, or SF₆, the Fermi level undergoes a moderate shift (ranging from −1.083 eV to +0.349 eV), with minimal changes in the band gap. Conversely, SO₂ adsorption induces a substantial downward shift of the Fermi level to −1.732 eV, accompanied by the emergence of a sharp partial density of states (PDOS) peak near the Fermi level (0–1.5 eV), indicating strong orbital coupling and significant charge transfer. Furthermore, recovery times calculated using classical formulas show that at room temperature and a frequency of 1 × 10⁶ Hz, the recovery time of GMV for SO₂ is 2.43 s, outperforming the other four gases and satisfying practical gas sensing requirements. Through comprehensive analysis of adsorption distances, electronic structure changes, and recovery times, GMV exhibits higher selectivity toward SO₂. Thus, GMV can serve as a sensing material for detecting GIS insulation faults associated with elevated SO₂ concentrations, offering a viable strategy for advancing online monitoring technologies in power systems. Full article

Different types of partial discharge (PD) cause varying degrees of insulation damage in gas-insulated switchgear (GIS), making accurate recognition of discharge types crucial for the safe and stable operation of GIS. The PD signal analysis method, including feature definition and extraction, is the foundation for recognizing discharge types. This paper first presents some measurement results of four PD types obtained on a GIS experimental platform. By analyzing the measurement results and corresponding physical mechanisms, we proposed two histogram-based features, which are discharge count versus phase histogram and discharge count versus amplitude histogram. To leverage the complementary advantages of these two features, distance-level fusion is achieved based on histogram distance. Following feature fusion, a distance-based k-nearest neighbors (KNN) classifier is used to recognize the four PD types. Compared with traditional feature fusion methods using feature concatenation, distance-level fusion improves recognition accuracy. The proposed PD type recognition method is also compared with two existing methods: one based on statistical features and another based on phase-resolved partial discharge (PRPD) image features. The results show that the proposed method achieves better recognition performance, with an accuracy of 95.8%. Full article

Gas-insulated switchgear (GIS) is a critical component of modern power systems. During operation, internal defects increase the probability of partial discharge and flashover within the insulation system, thereby constituting a major cause of equipment failure. Considering the diversity of existing GIS insulation condition monitoring methods, it is of great significance to systematically review and evaluate current monitoring technologies. This paper summarizes the detection principles and recent advances in electrical, acoustic, optical, modal analysis, and gas component analysis techniques. Through a comparative analysis of the advantages, limitations, and application scenarios of different methods, in conjunction with failure cases induced by typical GIS insulation defects, the primary bottlenecks faced by various condition monitoring technologies are discussed. Furthermore, future research directions for GIS insulation condition detection are outlined. This study provides a reference for the development of GIS insulation monitoring technologies and the formulation of efficient operation and maintenance strategies. Full article

The partial discharge (PD) caused by insulation defects of gas-insulated switchgear (GIS) threatens the secure and stable operation of power systems. Traditional PD pattern recognition methods exhibit limitations due to incomplete information utilization and unresolved correlations among characteristic parameters. Based on the partial discharge mechanisms of GIS, this paper establishes a GIS partial discharge simulation model using the finite element time-domain (FETD) method. The propagation rules and influence factors of ultra-high-frequency (UHF) signals are studied. Furthermore, a PD pattern recognition method based on a deep convolutional neural network (CNN) is proposed. Research results indicate that UHF signals generated by GIS partial discharge are significantly influenced by pulse current waveforms and discharge quantity. The peak-to-peak amplitude of the electric field (Epp) increases linearly with the current amplitude, while it decreases nonlinearly with increasing pulse width. The UHF signal remains a certain value while the pulse width exceeds a critical threshold (4 ns). The proposed CNN-based approach, utilizing full-wave UHF signals, overcomes the shortcomings of traditional methods reliant on manually extracted discrete feature parameters. Compared to other network architectures and optimization algorithms, the ConvNeXt-AdamW model demonstrates superior performance, achieving an average PD pattern recognition accuracy exceeding 96%. Full article