Gas Insulated Switchgear Research Articles

Defect Recognition for Partial Discharge Patterns of Gas Insulated Switchgear and Cable Joint Based on Deep Learning Methods

Defect Recognition for Partial Discharge Patterns of Gas Insulated Switchgear and Cable Joint Based on Deep Learning Methods

Enhanced Detection of SF₆ Gas Moisture Concentration in Gas-Insulated Switchgear Utilizing Tilted Fiber Bragg Grating Coated With Multilayer Graphene Oxide Films

Enhanced Detection of SF₆ Gas Moisture Concentration in Gas-Insulated Switchgear Utilizing Tilted Fiber Bragg Grating Coated With Multilayer Graphene Oxide Films

Design Optimization of Ferrite Rings for VFTO Mitigation

Very fast transient overvoltage (VFTO)-related problems are raising growing concerns in China with the development of ultra-high voltage. The practice of fitting ferrite rings on gas-insulated switchgear busbars for VFTO mitigation has long been proposed but few investigations on its' design optimization were conducted. Based on theoretical analysis of the simplified model, the optimal damping condition of ferrite rings is proposed, which is conducive to the type selection. The number of rings to be employed to ensure visible suppressing effects is given through simulations and the influence of the saturation is discussed. The impact on power frequency is analyzed to be negligible. Finally, comparisons are made between theoretical conclusions and existing experiment results, which approve the optimal damping condition and agree in the number of rings to be employed.

Fundamental Study of Spacer‐Insulation‐Performance Improvement by Nonlinear‐Resistance Material

Coating the surface of insulators used in a gas‐insulated switchgear (GIS) with nonlinear‐resistance material is expected to increase breakdown voltage by reducing the electric‐field concentration when metal particles adhere to the insulators. In this study, an epoxy resin with zinc oxide was used as a nonlinear‐resistance material. Tensile strength, adhesion by heat cycle test, hardness, and resistance to decomposition products of sulfur hexafluoride gas were evaluated for the nonlinear‐resistance material. It was confirmed that there were no problems with mechanical properties for application to GIS. The nonlinear‐resistance material was spray‐coated on actual post spacers, and the breakdown voltage was evaluated when metallic particles adhered to the post spacers. When the coating thickness was 20–35 μm, the breakdown voltage increased by about 8%. When the coating thickness was 50– 68 μm, however, the breakdown voltage increased by 23%, confirming that the breakdown voltage of an actual spacer improved. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Supervised Contrastive Learning for Fault Diagnosis Based on Phase-Resolved Partial Discharge in Gas-Insulated Switchgear

Supervised contrastive learning (SCL) has recently emerged as an alternative to conventional machine learning and deep neural networks. In this study, we propose an SCL model with data augmentation techniques using phase-resolved partial discharge (PRPD) in gas-insulated switchgear (GIS). To increase the fault data for training, we employ Gaussian noise adding, Gaussian noise scaling, random cropping, and phase shifting for supervised contrastive loss. The performance of the proposed SCL was verified by four types of faults in the GIS and on-site noise using an on-line ultra-high-frequency (UHF) partial discharge (PD) monitoring system. The experimental results show that the proposed SCL achieves a classification accuracy of 97.28% and outperforms the other algorithms, including support vector machines (SVM), multilayer perceptron (MLP), and convolution neural networks (CNNs) in terms of classification accuracy, by 6.8%, 4.28%, 2.04%, respectively.

Analysis of the calculation methods for shell circulating current and grounding current in 500 kV HGIS and the optimization of grounding scheme

The strong electromagnetic coupling between the wire and the shell of Hybrid Gas Insulated Switchgear (HGIS) will lead to a large induced current on the shell, which will further cause local heating of the outside shell. Moreover, the utilization of grounding wire to release this current may also lead to the problem of its heating and ground potential rise. This study is intended to further promote the calculation accuracy of the shell circulating current and grounding current of the 500 kV HGIS and also propose the proper grounding schemes. The simulation results show that the ground grid must be considered in the calculation model and the phase difference of the conductor current could be ignored when the three-phase spacing exceeded 7.5 m. Furthermore, the influences of shorting bars and ground grid on the simulation results were compared and analyzed, and the optimization of grounding scheme and corresponding suggestions were put forward. Generally, it was not recommended to erect shorting bars and auxiliary ground grids. But if the temperature rise exceeds the limit, the shorting bars can be added at the two ends of a HGIS branch and the auxiliary ground grid should be laid using materials with higher flow capacity.

Gas-insulated switchgear partial discharge acoustic–electric joint localisation method based on the Salp Swarm Algorithm and least squares estimation

Partial discharge (PD) is an early warning of gas-insulated switchgear (GIS) insulation faults. Consequently, accurate localisation of PD is crucial for the safe operation of GIS. The time difference of arrival method based on acoustic emission technology is more favorable for the engineering application of PD source localisation owing to its noninvasive characteristics and the small amount of required data. However, because PD ultrasonic signal propagation in GIS will introduce a variety of errors, current localisation algorithms suffer from problems of low accuracy and poor stability. Therefore, this study simulate and analyse the ultrasonic propagation characteristics as well as the sources of errors in GIS, based on which a joint acoustic–electric localisation method based on the Salp Swarm Algorithm is proposed, and an error-correction method based on least squares estimation is introduced. The localisation results show that the proposed method can effectively reduce the localisation error caused by multiple sources of error, and the mean localisation error can be reduced to within 20 mm in different structural gas chambers of GIS, which is more accurate and more stable compared with other methods, and it has good value for engineering application.

Engineering equipment conceptual design methodology: using function units configuration and schemes iterative solution

Engineering equipment can efficiently assist humans in completing engineering projects and reduce work intensity. As the difficulty and scope of operations increase, higher requirements are placed on function and integration. In product design, excessive user requirements and design constraints lead to discrete results, difficult to converge. Under multi-functionality and high integration constraints, traditional mapping sub-functions to structures one by one results in overabundant mappings, and discrete, redundant structures after mapping, poorly achieving integration and reducing subsequent iteration efficiency. To address these issues, this paper proposes a conceptual design methodology for engineering equipment with function units configuration and schemes iterative solution. First, the Fuzzy Quality Function Deployment (FQFD) method applies fuzzy theory to requirement-to-function conversion, overcoming ambiguity and subjectivity of user requirements. Second, with the goal of integrated structures, an improved FBS method, i.e. Function-Function Units-Structure (FFUS), aggregates sub-functions after decomposition into function units, and then maps to structures, improving engineering equipment integration. Moreover, the Interactive Genetic Algorithm (IGA) method involves users in an interactive process to generate an optimised design scheme, overcoming blind solution generation. Prototype design and simulation of 8VN1 Gas Insulated Switchgear (GIS) handing equipment demonstrate that the methodologies are effective in aiding engineering equipment conceptual design.

Simulation study of partial discharge UHF signal propagation characteristics in GIS

Insulation defects inside a Gas Insulated Switchgear (GIS) can generate partial discharges, and the current pulses caused by them can excite ultra-high frequency (UHF) electromagnetic waves. To study the attenuation characteristics of UHF signal propagation in GIS, simulation models of the straight cylinder, insulator, L-type, and T-type structures are established by CST simulation software. Then the propagation characteristics of partial discharge UHF signal are studied and analyzed. The simulation results show that the electromagnetic wave propagates in each structure inside the GIS with different degrees of attenuation. When passing through insulators continuously, the first insulator has the most serious attenuation; when passing through T-shaped structures, the attenuation of T-shaped vertical branches is more serious than linear branches.

Electric Field in Metal Stress Cone of Cable Quick Repair/Test Joint

In order to expedite power restoration after cable failures, a plug-in GIS (Gas Insulated Switchgear) intermediate joint is designed for cable quick repair/test. During the electric field simulation of this intermediate joint, significant electric field distortions were observed at the metal stress cones and the cable’s semi-conductive layer discontinuity, indicating potential weak points in insulation. To mitigate the electric field distortions within the joint, investigations were conducted on the impact of factors such as the gap width between the metal stress cones and the XLPE cable, the relative position of the semi-conductive layer discontinuity, the stress cone, and the winding length of high dielectric constant tape. The design and installation scheme of the metal stress cone wasa obtained through simulation, which provides a basis for the design and manufacture of the GIS quick repair/test joint.

Research on Denoising of GIS Ultra-High Frequency Partial Discharge Signal Based on VMD Combined with S-G Filter

Given the problem that it is easy to receive thermal noise interference from the electronic circuit and equipment circuit of the detection system in the current feature frequency detection of Gas Insulated Switchgear (GIS) equipment, this paper proposes an ultra-high frequency (UHF) signal noise reduction method based on variational mode decomposition (VMD) combined with Savitzky-Golay (S-G) filter and tests it through simulation. By using VMD combine with the S-G filter method, the signal-to-noise ratio (SNR) of signals can be effectively improved and the interference of white noise to signals can be reduced.

Simultaneous Partial Discharge Diagnosis and Localization in Gas-Insulated Switchgear via a Dual-Task Learning Network

Simultaneous Partial Discharge Diagnosis and Localization in Gas-Insulated Switchgear via a Dual-Task Learning Network

Characterization of internal discharge shock waves of gas‐insulated switchgears using shadowgraphy

AbstractThe gas‐insulated switchgear (GIS) is a vital component of a power system, and understanding the shock wave characteristics of its internal discharge is crucial to ensure the safe and stable operation of the entire system. In this study, a high‐speed shadowing technique is employed to obtain the shadowgraphs of a typical flow‐field evolution of sulfur hexafluoride (SF6) gas following the breakdown of a pin–plate gap inside a GIS. Experimental data were used to derive parameters, including the Mach number, propagation speed, distance of the discharge shock wave, and the temperature, pressure, density, and velocity of the SF6 gas after wave generation. With a 0.2‐mm discharge gap and 0.4‐MPa pressure, the discharge generates a spherical shock wave, centred at the contact between the discharge channel and electrodes, that propagates in all directions. The shock wave gradually weakens after 20 μs, propagating at near‐sonic speed, and its Mach number decreases from 2.92 to 1.15; similarly, its post‐wave parameters sharply decrease during the first 20 μs. Additionally, a significant reflection occurs when the shock wave propagates to the metal wall. The authors’ findings provide a valuable reference for detecting and diagnosing the internal discharge in GISs.

Cr decorated MoTe2 monolayer as C4F7N decomposed species adsorbent and scavenger: A first-principles study

C4F7N is a novel, dependable, and environmentally friendly insulation gas that can replace SF6 for high voltage insulation equipment. When extreme conditions such as partial discharge occur, the decomposition species of C4F7N following breakdown can have an undesirable impact on the functionality of insulated equipment, environmental safety, and human health. Based on the first- principles, this study enhances the capacity to adsorb and scavenge COF2, CF3CN, C2F5CN, and CF4 by decorating the transition metal Cr atom on the surface of pristine MoTe2 monolayer. The Cr atom can be as an electron donor (0.076 e) and convert the target molecule adsorption mode to chemisorption by greatly optimizing the band structure of the two-dimensional MoTe2 monolayer. The binding energy of the most stable modified structure is −1.330 eV. The C4F7N decomposition species' adsorption properties and electronic behaviors have been significantly improved by the surface decoration. Moreover, Cr-MoTe2 is thermally stable at high temperatures, so it can be applicated as a reliable target gas scavenger in gas insulated switch-gear (GIS) or other high-voltage insulation equipment for adsorption and scavenging.

3-D modeling of automatic pressure gelation process for manufacturing gas insulated switchgear spacer using bio-based epoxy composite

In existing power devices, various insulating parts are manufactured with petroleum-based epoxy resin. However, as petroleum-based resources are gradually depleted and environmental problems have emerged, research on replacing petroleum-based products with eco-friendly materials as insulators has been actively conducted. Therefore, in this study, a molding simulation study of the Auto Pressure Gelation (APG) process, one of the leading technologies, was conducted to manufacture a gas insulated switchgear (GIS) spacer using an environmentally friendly bio-based epoxy composite. Using the heat dissipation and degree of cure obtained through the dynamic differential scanning calorimetry (DSC) test, it was applied to the Kamal-Sourour model which is a representative curing dynamics empirical formula. In addition, the viscosity change during the curing process was measured using a rheometer and applied to the cross Castro-Macoskco model. The results obtained were in good agreement with the experimental values. The parameters calculated above and other physical property values were input into Moldflow software, and simulation was performed targeting the three-phase spacer. While changing the temperature setting of the mold, the change and distribution of flow, temperature, and curing degree inside the product were observed during the reactive molding process. As a result, it was found that there was a possibility of clogging of the mold entrance at some set temperatures, and it was possible to suggest improvement directions to solve this problem. Through this series of research process, it is considered that it will be of great help in improving the quality problems of molded products and the process accordingly.

Cross-domain open-set fault diagnosis using prototype learning and extreme value theory

The open-set cross-domain fault diagnosis scenario is similar to the practical industrial application. It assumes that a distribution discrepancy between the training (source) and testing (target) datasets exists, and the testing label set contains the training label set. The developed model transfers knowledge from the source domain to the share-class faults of the target domain and simultaneously rejects unknown fault samples. Previous studies have two shortcomings. First, feature alignment strategies only consider the alignment of the source and target-known distributions but ignore the segregation of the source and target-unknown distributions, resulting in unreasonable boundaries between known and unknown classes. Secondly, the similarity calculation for feature alignment relies on overconfident network predictions, resulting in biased similarities. To address these problems, this study employs two domain discriminators to clarify the boundaries between the known and unknown classes; one for aligning the source and target-known, and the other for segregating the source and target-unknown. In addition, an unbiased similarity calculation strategy using prototype learning and extreme value theory was proposed to quantify the contribution of each target sample to the domain adversarial training loss. Extensive experiments were conducted on a public bearing dataset and a laboratory gas-insulated switchgear vibration signals. The results outperformed those of other reported approaches, indicating that the proposed method is a promising model for practical industrial applications.

Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method

The utilization of ultraviolet (UV) absorption spectroscopy for monitoring the concentration of specific decomposition gas components in gas-insulated switchgear (GIS) can provide a means to assess its insulation status. Nevertheless, UV optical modules currently deployed in the field are susceptible to external interferences like ambient noise and equipment vibrations. Real-time spectral data acquisition often suffers from significant noise contamination, directly impinging on subsequent feature extraction and detection accuracy. This paper presents an optimized singular value decomposition (SVD) noise reduction method for mitigating noisy spectral signals. First, each singular value within the noisy signal is transformed into a component signal. Next, the highest frequency value in the signal serves as an indicator to characterize the signal. Finally, the primary frequency values are arranged based on the decreasing singular values of the original noisy signal. The singular value corresponding to the first primary frequency value surpassing a preset threshold is selected as the effective order for denoising. Random noise with varying intensities was intentionally introduced to the UV spectral signal of sulfur dioxide (SO2), followed by noise reduction procedures. It is shown that the improved SVD noise reduction algorithm proposed in this paper enhances the signal-to-noise ratio (SNR) by 18.02% and 16.86%, and reduces the root-mean-square error (RMSE) by 15.13% and 14.92%, respectively, compared with the singular value difference spectrum (SVDS) denoising method and wavelet transform denoising method under the condition of low SNR. Furthermore, there exists a linear relationship between the concentration of SO2 samples and the eigenvalues of the UV spectra, demonstrating a higher linear goodness with a coefficient of 0.99735. The denoising method proposed in this paper does not require the manual setting of various types of parameters, and has a better ability to deal with the noise of UV spectral signals in engineering sites with complex environments.

Intelligent defect diagnosis of GIS basin insulator via multi-source ultrasonic fusion

ABSTRACT To take advantage of ultrasonic-based non-destructive testing (NDT) and data-driven intelligent defect diagnosis, the current study proposes a feature tensor classifier based on multi-source ultrasonic fusion, to enhance the defect diagnosis adaptability and reliability for gas-insulated switchgear (GIS) basin insulator. First, multi-source ultrasonic signals are acquired by finite element modelling (FEM), describing the healthy states of the GIS basin insulator completely. Second, time of flight (Tof)-featured tensors are expressed by wavelet transform (WT), and used to create the datasets. Third, a deep learning-based feature tensor classifier is proposed, and concerned training, validation, and testing processes are carried out. Finally, the effectiveness of feature tensor extraction is evaluated, and the anti-noise performance of the Tof-featured tensor classifier is verified. The main contributions indicate that the Tof-featured tensor classifier can realise excellent diagnosis performance, the average accuracy is, respectively, 90.53%, 99.75%, and 100% in training, validation, and testing sets, while the signal tensor classifier has poor performance. In addition, three other noised datasets are applied, and the result shows that the anti-noise performance of the Tof-featured tensor classifier is feasible, when SNR is greater than 1 dB.

Class-relevant feature density estimator for open set fault classification of industrial equipment using vibration signals

Recently, the application of vibration signals to the fault diagnosis of industrial equipment has attracted increased attention. Open set recognition allows deep networks to detect unknown faults while maintaining a high-classification accuracy for known faults, which is necessary for practical industrial applications. Existing distance- and probability-based open set recognition methods show significant potential in handling this challenging task. In this study, we combine a deep backbone network and probabilistic model to construct a class-relevant feature density estimator (CRFDE) that integrates the advantages of distance- and probability-based methods. Specifically, a loss function is developed to enable the backbone network to extract the class-relevant features and probabilistic model to estimate them, where the employed probabilistic model is improved from variational auto-encoder (VAE) to class-conditional VAE (CCVAE) that can estimate the class-conditional data densities. The output class-relevant feature densities of CCVAE are treated as special distances to determine the class belongingness of the input samples. We validated the proposed CRFDE on two public motor bearing datasets and one laboratory gas-insulated switchgear vibration signal dataset. The results revealed that the proposed CRFDE achieved higher F1 scores than the existing methods.

Stress Measurements of GIS Epoxy Composite with Transverse Waves Based on the Ultrasonic Pulse-Echo Method

The mechanical reliability of the gas-insulated metal-enclosed switchgear (GIS) is critical to the safe and stable operation of a power system. The internal stress induced in GIS insulators, which typically lead to cracking, can degrade the mechanical properties and compromise the reliability of the GIS. In this work, ultrasonic nondestructive measurements of the internal stress in a GIS 126 kV epoxy composite with the transverse waves are demonstrated for the first time. Our results show that the velocities of ultrasonic transverse waves increase linearly with the compressive stress and decrease linearly with the tensile stress. The acoustoelastic coefficients are −1.18 × 10−4/MPa and −1.96 × 10−4/MPa for measurements with transverse waves polarized vertically and parallelly to stress, respectively. Based on these acoustoelastic coefficients, the internal stresses are calculated, and the maximum absolute values of relative errors are 35.4% and 26.3% for measurements with the transverse wave polarized vertically and parallelly to stress, respectively.