Gas Insulated Switchgear Research Articles

Fault Diagnosis of Gas Insulated Switchgear Isolation Switch Based on Improved Support Vector Data Description Method

To improve the efficiency and precision of fault diagnosis for isolation switches within Gas-insulated switchgear (GIS), this study introduces an advanced technique utilizing an enhanced support vector data description (SVDD) algorithm. Initially, various operational states of the GIS isolation switch are simulated, and the corresponding vibration signals are captured. Subsequently, both the entropy and time-domain features of these signals are extracted to construct a multi-dimensional feature space. High-dimensional feature datasets are then reduced in dimensionality using the kernel principal component analysis (KPCA) method. Furthermore, the conventional SVDD algorithm is modified by incorporating a penalty factor, which allows for a more adaptable classification boundary. This adaptation not only focuses on positive samples but also considers the influence of selected negative samples on the classification hypersphere. Finally, the collected experimental data are classified and predicted. The results indicate that this GIS fault-diagnosis approach effectively overcomes the limitations of traditional methods, which are heavily dependent on training sample data and demonstrate poor algorithm generalization performance. This method is proven to be applicable for the fault diagnosis of isolation switches in GIS.

Au doped MXene for sulphurous gases sensing: a DFT study

Sulfur-containing gas reflects the internal operation of SF6 gas-insulated switchgear and is of great importance for the insulation fault diagnosis of equipment. Based on density functional theory (DFT), several sulfur-containing gases are selected as the target detection gases, and Au-doped Ti3C2Tx MXene is used as the sensing material to carry out adsorption simulation in this paper. These results suggest that the O and OH terminated Au@Ti3C2Tx have larger adsorption energies as well as charge transfer for SO2 and SOF2, and furthermore, combined with density of states analysis and molecular frontier orbital analysis, it’s found that gas adsorption can lead to changes in the density of states near the 0 eV and changes in the frontier orbital band gap, which can be used as SO2 and SOF2 gas sensor. These provide initial insights into the application of Au@Ti3C2Tx in the monitoring of electrical equipment, evaluating the working condition of the SF6 gas-insulated electrical equipment by detecting SO2 and SOF2.

Multi-gas photoacoustic sensor using multi-mode demodulation.

Modulation technology is the necessary means for generating periodic acoustic waves in photoacoustic gas detection, primarily including intensity modulation and wavelength modulation. In multi-gas detection, when multiple lasers employ the same modulation technique, current technologies include time-division multiplexing (TDM) for measurements at different times and frequency-division multiplexing (FDM) for simultaneous measurements; when multiple lasers employ different modulation techniques, the only available technology is TDM with measurements conducted at different times, and whether simultaneous measurement can be achieved has not yet been verified. We propose, for the first time, a multi-gas photoacoustic sensor using multi-mode demodulation. This sensor employs multi-mode frequency division multiplexing (MMFDM) technology to separate and demodulate the multi-mode photoacoustic signal, thereby enabling the simultaneous measurement of multiple gases under different modulation techniques. To demonstrate the feasibility of this method, we used SO and HF, the SF decomposition products in gas-insulated switchgear (GIS), as target gases and simultaneously detected their mixture using different modulation modes. Experimental results show that when the frequency difference is 10 Hz, multi-mode photoacoustic signal can be successfully separated, with the minimum detection limits for SO and HF reaching 117.9 ppb and 65.5 ppb, respectively. This study is the first to validate the separability of multi-mode photoacoustic signal and achieve multi-gas simultaneous measurement under multi-mode modulation, thereby eliminating the limitations of modulation mode in simultaneous photoacoustic multi-gas detection.

Mechanical wear characteristics of 170 kV gas-insulated switchgear circuit-breaker contacts before arc initiation: An accelerated lifetime test approach

Mechanical wear characteristics of 170 kV gas-insulated switchgear circuit-breaker contacts before arc initiation: An accelerated lifetime test approach

Micro-discharge in GIS/GIL induced by scratch defect on the surface of insulation pull rods

Abstract The insulation pull rod (IPR) plays a crucial role in gas insulated switchgear (GIS) and gas insulated transmission line (GIL), which must withstand high impulse voltages to cope with frequent switching operations under working conditions. During the manufacturing process, micro-defects may occur on the surface of insulation pull rods, which may cause micro-discharge or even breakdown. It is difficult to carry out experiments to study the microscopic characteristics of the micro-discharge induced by micro-defects on the surface of the insulation pull rod. In order to study the relationship between the micro-discharge and the micro-defects. Firstly, the characterization of the scratch defect based on Micro-CT (micro computed tomography) and white light interference are presented. Secondly, a coaxial DC SF6 corona discharge model is established to obtain the curve related to the density of charged particles in SF6 and the electric field. Finally, we combined the reconstruction of the defect model in the first step with the curve obtained in the second step to simulate the micro discharge around the scratch defect, and provided the micro-discharge in pure SF6 caused by the scratch defect. The phenomenon of micro-discharge induced by the scratch of insulation pull rod is explained. The oscillation phenomenon of internal micro-discharge in scratch defects under high electric field background was discovered.

High permittivity electroluminescence coating for improving flashover strength and visualizing surface defects

Abstract In gas-insulated switchgear (GIS), the metallic particles on spacers initiate discharges and significantly reduce flashover strength. It is crucial to sensitively detect the surface defects and improve the flashover strength. In this work, a high permittivity electroluminescence coating, with ZnS:Cu as fillers and cyanoethyl cellulose (CEC) as polymer matrix was developed to improve flashover strength and visualize surface defects. The dielectric constant, flashover strength, and electroluminescence properties of ZnS:Cu/CEC coatings with different concentration of ZnS:Cu fillers were tested and the underlying mechanism was investigated. Due to the strong-polarity cyanide group in CEC and the effect of ZnS:Cu fillers on polymer chains, a high-permittivity coating was obtained, which can effectively mitigate the electric field distortion on spacers, and improve the flashover performance. For the basin insulator, maximum increments of flashover strength were realized by doping 12.5 wt% ZnS:Cu fillers, with 11.36%, 31.22%, and 5.83% improvements in air, 0.3 MPa air, and SF6 gas respectively. Moreover, the electroluminescence coating can effectively indicate the location of defects and the distribution of electric field on the surface of insulators. This paper provides a new insight into the electroluminescent materials used in non-destructive self-testing of surface defects and enhancement of flashover strength.

The gas-sensing mechanism of Nb2C monolayer for SF6 decomposition based on the DFT study

Abstract During fault analysis of gas-insulated switchgear (GIS), continuous monitoring of the gases produced by the decomposition of SF6 is critical to the safe operation of the equipment. Although a variety of gas detection technologies are currently available on the market, low-power gas detection devices for SF6 decomposition products are still in the development stage, and technological advances in this area are of great significance for improving the reliability of GIS systems. Based on the density functional theory (DFT), the Nb2C crystal surface structure was established and six adsorption structures of SO2, H2S, SOF2, SO2F2, HF and CO gas molecules on Nb2C crystal surface were constructed by geometrical optimization in this paper. The gas-sensitive properties of each adsorption system were explored in terms of adsorption energy, charge transfer, electron density, density of states and recovery time. The results showed that the Nb2C crystal surface was unfit HF and CO gases detection, both of which showed weak physical adsorption; the adsorption energies of SO2, H2S, SOF2, and SO2F2 on the Nb2C crystal surface were −1.846 eV, −1.081 eV, −5.270 eV, and −10.582 eV, respectively, and all of them were strong chemical adsorption. It was further shown by theoretical recovery time calculations that the Nb2C crystal surface can act as SOF2 and SO2F2 gas scavengers, and are able to desorb H2S (4.69 s) and SO2 (2.26 s) gases by appropriately increasing the temperature, but the Nb2C crystal surface is more suitable for use as a low-power gas-sensitive material for H2S gas detection. Therefore, Nb2C is anticipated to be a promising material for high response, low-power consumption and fast recovery for H2S gas detection in SF6 decomposition gas.

Mechanism insights and experimental feasibility of using boron nitride nanocones for rapid adsorption and degradation of SF6 decomposition compounds

Gas-insulated switchgear (GIS) employs sulfur hexafluoride (SF6) as an insulating medium to shield electrical gadget. However, SF6 can decompose under sure situations, generating dangerous sulfur-based totally compounds which include SO2, SOF2, and SO2F2. These byproducts pose enormous dangers to both protection and environmental integrity. Efficiently adsorbing and disposing of those compounds is critical for ensuring operational reliability and reducing environmental dangers. This study investigates the adsorption and degradation mechanisms of SF₆ decomposition compounds (SO₂, SOF₂, and SO₂F₂) on boron nitride nanocones (BNNCs) using density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. Our comprehensive analysis covers five distinct systems, exploring individual and combined adsorption scenarios. The findings reveal that the apex of BNNCs plays a crucial role in the adsorption process, showing high efficiency in adsorbing SO₂ (adsorption energy − 1.22 eV) and facilitating the catalytic breakdown of SOF₂ (adsorption energy − 1.57 eV). The positively charged potential at the nanocone’s apex significantly influences the dissociation and subsequent adsorption of fluorine atoms, with an energy barrier for F dissociation at the apex (1.8 kcal/mol) much lower than at the sidewall (5.3 kcal/mol). In gas mixtures, SO₂ preferentially binds to the apex region of BNNCs, with a bond length of approximately 1.38 Å. BNNCs demonstrate superior adsorption capabilities for SO₂ and SOF₂ compared to other boron nitride nanostructures, with adsorption energies up to 89% higher. The electron transfer analysis reveals that BNNC complexes act as potent electron donors, particularly in the case of BNNC@3SO₂F₂. Additionally, BNNCs show significant potential as sensors for detecting SO₂F₂, with a rapid recovery time of 4.67 ps and a notable decrease in the Fermi level energy to -4.97 eV upon adsorption. The study also provides insights into the angular distribution and charge density difference profiles, offering a detailed understanding of the adsorption mechanisms. These findings have important implications for improving the safety and efficiency of gas-insulated switchgear (GIS) and contribute to the development of more effective environmental protection solutions in electrical power systems.

Multi-source partial discharge pattern recognition in GIS based on Grabcut-MCNN

Partial discharge (PD) surveillance constitutes a pivotal methodology for diagnosing insulation failures in electrical equipment. Enhancing comprehensively the precision of identifying PD anomalies in Gas Insulated Switchgear (GIS) is of paramount significance for ensuring the steady functioning of power grids. This study introduces a novel framework that integrates Phase-Resolved PD Graph Segmentation (PRPD-Grabcut) with a tailored MobileNets-based Convolutional Neural Network (MCNN) to classify GIS-related PD issues. Leveraging image segmentation via PRPD-Grabcut, crucial features are extracted from PRPD diagrams, which then facilitate the construction of the MCNN model. This model employs depth-wise separable convolutions alongside inverted residual architectures to tackle the vanishing gradient dilemma inherent in Deep Convolutional Neural Networks (DCNNs) during GIS PD pattern discernment. Upon the model's subsequent training and validation, empirical evidence illustrates that the PRPD-Grabcut-MCNN hybrid significantly alleviates the computational load and storage requisites of the model, concurrently enhancing the recognition precision and expediting the training process of the neural network. Relative to diverse established lightweight neural network architectures, MCNN manifests superior performance in terms of recognition accuracy, reduced cross-entropy loss, and expedited training duration.

Discharge modes evolution characteristics of metal particle on the spacer surface in AC gas insulated switchgear

Abstract Flashover faults on gas insulated switchgear (GIS) insulators induced by metal particles occur frequently. Previous studies have obtained the characteristics of partial discharges (PDs) induced by metal particles on spacer surfaces, but these characteristics cannot explain the detection failure of ultra-high frequency (UHF) online monitoring. To enable further study of the PD characteristics induced by surface metal particles, the space electric field and a very-high-sensitivity pulse current (PC) measurement system were established. The electric field and PC characteristics of a PD induced by metal particle on the spacer surface of a 126 kV GIS were obtained. Two PD modes were found to be induced by surface metal particles. In addition to the typical pulse discharge (TP), there is a micro-discharge group (MG) mode with low apparent charge and long duration. The average apparent charge of the MG mode is approximately one-tenth of that of the TP at 0.4 pC. Its duration may extend to the millisecond level, causing significant distortion of the spatial electric field while hardly producing UHF signals. Moreover, increasing the applied voltage will increase the proportion of the MG within the total discharge, where this proportion can reach more than 90% before flashover, and the proportion of the discharge pulses that generates UHF signals is as low as 1%. The MG generation mechanism is analysed, the ion group stranded on the spacer surface by the TP changes the local electric field at the tip of the metal particle, which reduces the development length and apparent charge of the MG. Low apparent charge is cleared easily by the background electric field and thus the discharge interval is very short. This paper can provide an important basis for revealing the mechanism of GIS spacer surface discharge induced by metal particles and solving the effectiveness of PD monitoring devices.

Theoretical Investigation of Rhodium-Decorated Gallium Nitride Nanotubes for Sulfur Hexafluoride Decomposition Products Sensing and Scavenging Applications.

Gas-insulated switchgear (GIS) plays an important role as a modern power distribution device in power plants and power stations, which is commonly filled with SF6 insulating gas. During the equipment operation, the inevitable partial discharge causes SF6 to be broken down into gas (SF4, SOF2, SO2, and H2S), which degrades the insulation performance of the GIS. This paper is devoted to the detection of partial discharge and the removal of SF4 and SOF2, which are not conducive to insulation, by exploring new gas-sensing materials for characteristic gas detection. Based on first-principles calculation, on the one hand, the most stable adsorption configurations of rhodium-decorated gallium nitride nanotubes (Rh-GaNNTs) and gas adsorption systems were obtained. On the other hand, the doping and adsorption mechanisms were analyzed by band structure, density of states, deformation charge density, and molecular orbital theory. Subsequently, the gas-sensitive performance of Rh-GaNNTs for these four impurity gases was evaluated by analyzing the sensing response and recovery time. The adsorption stability and recovery time of Rh-GaNNTs to these gases are ranked as SF4 > SOF2 > SO2 > H2S; the order of influence of gas adsorption on sensitivity response is H2S > SO2 > SF4 ≈ SOF2. Calculation results show the potential of Rh-doped surfaces as reusable H2S and SO2 sensors and suggest their use as gas scavengers to remove SF4 and SOF2, especially SOF2.

Ultratrace CO/SF6 Detection System Based on Differential Fourier Transform Infrared Spectroscopy Combined with the Weighted Sine Spectral Reconstruction Convolutional Neural Network (WSSR-CNN) Model.

Carbon monoxide (CO), as an indicator gas for fault diagnosis of gas-insulated switchgear, has the strongest absorption coefficient in the mid-infrared region, making Fourier transform infrared (FTIR) spectroscopy an ideal method for its concentration detection. However, there is extremely abundant absorption of SF6 in this region, resulting in a great challenge for CO/SF6 detection. To address this challenge, we report a high sensitivity online detection system for ultratrace levels of CO, which combines differential Fourier transform infrared (DFTIR) spectroscopy with a weighted sine spectral reconstruction convolutional neural network (WSSR-CNN). The differential technique is first introduced into FTIR for baseline correction of CO absorption signals. The novel WSSR method achieves feature extraction and denoising of weak spectral signals in strong interference by discretizing interfering signals and enhancing target signals. On this basis, the detection of CO concentration is realized using the CNN model. Finally, WSSR-CNN is compared with four other methods. Results showed that WSSR-CNN outperforms all four models with evaluation index R2 reaching 0.99982 and 0.99999 in the range of low concentration (0.096-0.986 ppm) and high concentration (1.984-52.193 ppm) and mean absolute percentage error of 0.97% and 0.22%, respectively. The lowest detection limit of the system at 1σ is 13 ppb, which is the best result reported so far for detecting CO/SF6 concentration based on FTIR.

A DFT study of SF6 decomposition products (H2S, SO2, and CS2) adsorption and detection on Pd-ZnO/SnS2 ternary composites

Real-time and accurate detection of SF6 decomposition products is a crucial approach to diagnosing internal faults of gas-insulated switchgear (GIS). However, the limited sensitivity and selectivity of SnS2 gas sensors have hindered their further development and application. This study employs density functional theory to design the optimal Pd-ZnO/SnS2 monolayer structure and investigate its adsorption behavior toward H2S, SO2, and CS2. The findings indicate that Pd atom doping and ZnO nanoparticle incorporation significantly enhance the conductivity of the SnS2 monolayer, reducing the band gap to 0.54 eV and lowering the work function by 5.019 eV. Regarding gas adsorption and sensing, the Pd-ZnO/SnS2 monolayer outperforms intrinsic SnS2 and ZnO/SnS2, showing the order in which SF6 decomposition products are selected is: CS2 > SO2 > H2S. Additionally, the sensitivity and recovery time of Pd-ZnO/SnS2 as a gas sensor were evaluated, confirming its excellent sensing performance for SO2 and CS2 detection. This study provides theoretical insights to advance the application of gas sensors in online insulation equipment monitoring.

Upper limit assessment of GIS equipment failure based on time series model and deep learning algorithm

If a sudden fault of gas-insulated switchgear is not handled in time, it will delay production and cause certain economic losses to the enterprise. Therefore, this study proposes an upper limit assessment algorithm for equipment faults based on time series model and deep learning. The irregular fluctuation components in the time series model are stabilized by using empirical mode decomposition, and the long short-term memory network in the deep learning algorithm is introduced to combine the two. The equipment fault data is obtained by the joint algorithm. After empirical mode decomposition, the intrinsic mode function component of the data signal is obtained, and the sensitive component is extracted from it. The relationship value and mutual information between it and the original data signal are analyzed to complete the fault data feature extraction. In the process of equipment fault upper limit assessment, the parameter optimization and convergence are controlled, and the fault deep learning model is constructed. After the refraction coefficient and reflection coefficient are determined, the upper limit assessment of equipment faults is realized. The proposed method is experimentally tested. The results show that the proposed algorithm has an ideal upper limit assessment result of faults, and the obtained curve has a high degree of fit with the actual result curve. It can be proved that the proposed algorithm can scientifically evaluate the upper limit of equipment faults and has certain application value.

Initial Characteristics of Submillimeter Metal Particles in GIS under Impact Vibration

As a critical component within power systems, Gas Insulated Switchgear (GIS) is notably susceptible to insulation degradation due to submillimeter metal particles, compromising its operational safety and stability. Moreover, impact vibrations induced by circuit breaker operations can dislodge particles that typically adhere to the interior walls, causing them to become airborne and thereby intensifying their movement and discharge activities. To investigate this phenomenon, this study establishes a testing platform where alternating current (AC) voltage is superimposed with impact vibration. A camera system, interfaced with an upper computer, is used to capture the real-time motion behavior of the particles. This study focuses on characterizing the initial movement of submillimeter metal particles of varying sizes under the influence of impact vibration by analyzing critical voltages, such as the initial lift-off voltage, stable jumping voltage, and extinction voltage. Furthermore, the effects of introducing large, centimeter-scale spherical metal and rubber particles on these initial characteristics are examined. The findings provide crucial insights into the initial behavior of submillimeter metal particles in GIS, particularly in relation to circuit breaker operations.

SF6 Gas Humidity Prediction Model Based on Deep Learning

SF6 gas is widely used in Gas Insulated Switchgear(GIS) as an insulating and arc extinguishing medium in electric power industry. However, SF6 gas humidity has a significant impact on the performance and reliability of GIS equipment. The accurate prediction of humidity level is one of the keys to ensure the long-term stable operation of GIS equipment. Traditional humidity prediction methods are often limited by model complexity and data processing ability, which is difficult to meet the actual demand. In this paper, deep learning, as a powerful data-driven approach, shows great potential in gas humidity prediction. An efficient SF6 gas humidity prediction model for GIS equipment is constructed based on deep learning algorithm. The deep learning model can learn complex feature representations from a large number of historical data, and then accurately predict SF6 gas humidity.

Design of Modified UWB Microstrip Antenna for UHF Partial Discharge Sensor

The development of printable ultrahigh-frequency (UHF) antennas as partial discharge (PD) sensors for high-voltage equipment has been extensively studied. However, achieving ultrawideband (UWB) UHF PD sensors frequently requires larger sizes, unsuitable for certain applications requiring compact sensors for dielectric windows in HV equipment. This research objective is to obtain PD sensors with a wider bandwidth (0.3–3 GHz) and a compact size fitting a less-than-100mm-length gas-insulated switchgear (GIS) dielectric window. A circular patch microstrip antenna (CPMA) was chosen for its small size and potential for UWB performance. This paper discusses the design modification of the CPMA to obtain a wider bandwidth for PD detection in GIS. Simulations and lab-scale experimental verifications were conducted to evaluate the optimized sensor. The modified sensor, with a size of 60 × 73 mm², achieved a bandwidth of 3.08–3.14 GHz, a reflection coefficient of -44 dB, and several resonant frequencies of 0.3–2.3 GHz. This is a seven-time wider bandwidth compared to earlier bowtie antennas while keeping a dimension of less than 100 mm². These properties allow for efficient PD detection in GIS and other insulating media. Experimental results indicate the sensor's capacity to reliably detect and analyze PD signals while responding appropriately to variations in voltage. Doi: 10.28991/ESJ-2024-08-05-03 Full Text: PDF

Impact of X-Ray Irradiation on Discharge Inception in Gas Insulated Switchgear: A Simulation Investigation

Impact of X-Ray Irradiation on Discharge Inception in Gas Insulated Switchgear: A Simulation Investigation

Mutitask Learning Network for Partial Discharge Condition Assessment in Gas-Insulated Switchgear

Mutitask Learning Network for Partial Discharge Condition Assessment in Gas-Insulated Switchgear

Design and optimization for GIS UHF-Overvoltage composite sensor

Gas insulated switchgear (GIS) is widely used in power systems. Various built-in sensors have been designed to monitor internal partial discharges (PDs) and overvoltage, ensuring the safe operation of GIS. This paper presents a composite sensor for simultaneous measurement of PD and voltage, along with an effective optimization method that utilizes a Gaussian process (GP) surrogate model and self-adaptive evolution to optimize the complex composite sensors evaluated through simulations. The optimization method realizes accurate prediction of complex problems during the iteration with fewer training samples and is applied to the designed composite sensor, which requires determining additional parameters compared to the conventional sensors. Through iterations, the effective height of the composite sensor is significantly increased while maintaining a required measuring bandwidth. A test platform is constructed to assess the performance, and results demonstrate that the optimized sensor exhibits satisfying performance measuring both UHF PD signals and overvoltage in GIS. This study provides a design method for UHF-related sensors characterized by structural complexity.