Gas Insulated Switchgear Research Articles

Analisis Kebocaran Gas SF6 Terhadap Kecepatan Busur Api dan Tegangan Tembus pada PMT di GISTET Saguling

Gas Insulated Switchgear Extra High Voltage (GISTET) is a power system component that has a function as a distribution center (transmission) that connects the extra high voltage transmission system through distribution channels or substations. 
 
 Based on the installation of GISTET equipment, relying on hexafluoride gas (SF_6) as an insulating medium on the equpment. Equipment at GISTET is not always in good condition, GISTET also often experiences problems. One of the distrubances that GISTET often experiences is the leakage of SF6 gas at the PMT (circuit breaker). This requires a treatment called a GISTET overhaul. So in this paper the author will analyze the SSF_6 gas leak as insulation on the PMT (circuir breaker) at GISTET Saguling.
 
 According to the results of the calculations in this final project, the gas pressure of SF_6 and the breakdown voltage of PMT are interrelated. At the pressure when the gas leak occurs is at a value below 5kgf/cm2it is able to with stand the voltage. Penetrating with a high value so it is very effective to reduce arc fire. In addition, the SF_6 gas pressure is also related to the arc speed because the lower the pressure value at the time of gas leakage, the arc speed decreases, so the arc will be slow to muffle.
 

DFT Insight to Ag2O Modified InN as SF6-N2 Mixture Decomposition Components Detector

In gas-insulated switchgear (GIS), partial discharge (PD) can be monitored by detecting sulfur hexafluoride-nitrogen (SF6-N2) decomposition components. In this paper, silver oxide (Ag2O) modification was introduced to improve the gas-sensing properties of graphene-like indium nitride (InN). The adsorption process of NO2, SO2F2, SOF2 and SOF4 on Ag2O-InN was simulated based on the first principles calculation and density functional theory (DFT). The gas sensing mechanism was revealed by density of states theory and molecular orbital theory. It is found that Ag2O doping greatly improves the adsorption properties of InN to NO2 and SOF2 molecules. The adsorption capacity of Ag2O-InN to the four gas molecules is: NO2 > SOF2 > SOF4 > SO2F2. All adsorptions can proceed spontaneously, and the gas molecules are electron donors and Ag2O-InN is an electron acceptor. Through the analysis of recovery time, it is found that NO2 is difficult to desorb from the substrate due to the significant adsorption energy of −2.201 eV, while SOF4, SOF2 and SO2F2 have a moderate adsorption energy of −0.185 eV, −0.754 eV and 0.173 eV and extremely short desorption time. The conductivity of the whole system changed after these four gases were adsorbed on the Ag2O-InN monolayer. In summary, Ag2O-InN can be used as NO2 adsorbent and gas sensors to detect SOF4, SOF2 and SO2F2. This paper provides a method for on-line monitoring of partial discharge in GIS.

A Domain Adaptive Deep Transfer Learning Method for Gas-Insulated Switchgear Partial Discharge Diagnosis

Intelligent fault diagnosis methods, especially convolutional neural network (CNN), have made significant progress in gas-insulated switchgear (GIS) partial discharge (PD) diagnosis, which are attributable to two reasons: 1) the training and testing samples come from the identical distribution; 2) there are massive labeled data with PD information. However, owing to the specific operating conditions of GIS, collecting massive samples from the same distribution is difficult in field conditions. With the purpose of resolving the data dilemma of conventional diagnosis methods in the field, we propose a domain adaptive deep transfer learning (DADTL) CNN for small samples GIS PD diagnosis. First, we adopted a CNN to automatically extract transferable features from PD samples. Then, a multilayer DADTL is developed to reduce the marginal and conditional distribution of learned transferable features, and Sliced Wasserstein distance (SWD) is employed as a penalty to reduce the negative migration. The experimental verification was performed on experimental and on-site GIS PD datasets. The results show that DADTL CNN can effectively achieve a high-accuracy diagnosis of GIS PD for small samples. Compared with other methods, the DADTL CNN can achieve more accurate and robust GIS PD diagnosis for on-site small samples.

Knowledge-Driven Recognition Methodology of Partial Discharge Patterns in GIS

Recognition of partial discharge (PD) patterns in gas insulated switchgear (GIS), as a basis of fault diagnosis, provides essential information for the condition assessment of GIS. However, traditional recognition methods are limited to handcrafted feature extraction or are extremely sensitive to the quantity and quality of datasets. Therefore, this article proposes a knowledge-driven algorithm, composed of the feature space and the knowledge space, to automatically extract PD features and improve the performance on noised, insufficient, and imbalanced datasets. First, the deep residual network (ResNet) in feature space extracts features from raw signals. Second, in knowledge space, the graph convolutional network (GCN) extracts additional information from the knowledge graph, which compensates for the missing information of original datasets. Finally, the algorithm recognizes patterns by ranking similarities between feature vectors and knowledge vectors. Verified by the comparison experiment, the proposed algorithm outperforms traditional methods with the accuracy of 99.58% on the experimental dataset and 95.67% on the online-detected dataset. Moreover, the accuracy of the proposed algorithm achieves 88.79% and 70.37% on noised and insufficient datasets, respectively, while the F-measure is higher than those of the comparison methods by 12.95% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 18.69% on imbalanced datasets.

Low-temperature monitoring of SF6 decomposition products based on CeO2@MWCNTs composite sensor

The SF6 gas in gas-insulated switchgear (GIS) systems decomposes into SO2F2, SOF2, SO2, and H2S under abnormal conditions such as partial discharge. The detection of these decomposition gases with high sensitivity is essential for the safe operation of GIS devices. Herein, we report the synthesis of cerium oxide and multiwalled carbon nanotube composite (CeO2@MWCNTs). The CeO2@MWCNTs composite was characterized by X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectroscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, and Brunauer–Emmett–Teller analysis. The CeO2@MWCNTs composite sensor provided a response value of 7.7 at 100 ppm of SO2F2 at a low operating temperature (50 °C), and the response and recovery features of the CeO2@MWCNTs composite sensor were recorded at 43 and 28 s, respectively. Moreover, the composite sensor exhibited excellent stability after two months. The developed CeO2@MWCNTs composite sensor can offer an exceptional platform for gas sensing applications in diverse fields.

Optimized Denoising Method for Weak Acoustic Emission Signal in Partial Discharge Detection

Acoustic emission (AE) technology can predict the occurrence of partial discharge (PD) faults, which is used to improve the safe operation level of gas-insulated switchgear (GIS) equipment. However, the strong noise interference from the production site is still the main factor affecting the identification accuracy. In this study, a simplified model is designed to approximate the accumulation of free metal particles on the surface of the GIS internal insulation structure, and white noise of various intensities is added to the collected PD-induced AE signals to simulate the background interference. The results prove that the proposed denoising method can achieve a better denoising effect in various signal-to-noise ratio (SNR) conditions. In particular, in the case of low SNR, the recognition accuracy of the accumulation degree of metal particles has been improved by more than 15%.

Prediction of Impulse Breakdown Voltage in Dry Air at High Pressure Using Volume-Time Theory

Dry air is promising as an alternative insulation medium for SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> gas in high-voltage equipment. Since its dielectric strength is about one-third of that of SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> gas, one of the most effective ways to apply dry air to high-voltage gas-insulated switchgears (GISs) is to further increase gas pressure. In this article, impulse breakdown strength of dry air at high pressure under quasi-uniform electric field is measured. Furthermore, the probability distribution of breakdown voltage is predicted using the volume-time theory in consideration of initial electron generation probability in gas gap and field electron emission from an electrode. The predicted distribution of breakdown voltage by the volume-time theory is in line with the experimental results.

GIS의 수명특성 분석을 통한 고장대수 예측에 관한 연구

In the past, in accordance with the rapid development of industrial society, power facilities were replaced in order to expand the power grid and upgrade the performance. However, as the modernization of the industrial society is saturated, the replacement speed of facilities is slowing down, and the aging equipment is increasing. Therefore, interest in how to manage aging facilities is increasing, and research on the asset management of power facilities is required to establish a plan to efficiently operate facilities that are already in operation. It is necessary to diagnose the status of the facilities and analyze how the condition of the facilities will change in the future through an analysis of the remaining life. In order to establish the process, research on the statistical life of assets should be preceded. However, in the case of South Korea, power facilities are replaced before failure occurs in order to improve the reliability of power grid. Therefore, there is insufficient data to estimate the statistical life power assets. In this paper, a study on a system that analyzes the statistical lifetime using the data of gas insulated switchgear (GIS) published abroad and predicts the number of failures over time using the hazard rate.

Gas sensing properties of Mg‐doped graphene for H2S, SO2, SOF2, and SO2F2 based on DFT

AbstractSulfur hexafluoride decompositions have been studied to analyze their adsorption properties on pristine graphene (PG) and Mg‐doped graphene (MgG). First of all, after calculating the formation energy of three Mg doping sites, the T doping site of Mg‐doped graphene is the most stable one. Then, several characteristic structures with different orientations and positions of the gas molecules have been carried out to adsorb on PG and MgG, respectively. By calculating the adsorption energies and distance, the most stable adsorption structure of each gas molecule is obtained. In addition, transferred charge (Qt), the density of states (DOS), the energy of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) were used to further analyze the conductivity change and chemical stability of each adsorption system. The results indicate that the adsorption interactions of H2S, SO2, SOF2, and SO2F2 on PG are weak. H2S adsorbed on MgG presented physical adsorption, while the adsorption behaviors of SO2, SOF2, and SO2F2 on MgG are chemisorption. And the adsorption strength was SO2F2 &gt; SOF2 &gt; SO2. In short, MgG shows better selectivity and higher sensitivity to SO2, SOF2, and SO2F2 than PG, demonstrating that the MgG material can be used as suitable gas sensing equipment based on SF6 decomposition products detection, which provides a meaningful guide of alkaline earth metal‐doped graphene in the detection of partial discharge and partial overheat in gas‐insulated switchgears (GIS).

A Novel Method for Pattern Recognition of GIS Partial Discharge via Multi-Information Ensemble Learning.

Partial discharge (PD) is the main feature that effectively reflects the internal insulation defects of gas-insulated switchgear (GIS). It is of great significance to diagnose the types of insulation faults by recognizing PD to ensure the normal operation of GIS. However, the traditional diagnosis method based on single feature information analysis has a low recognition accuracy of PD, and there are great differences in the diagnosis effect of various insulation defects. To make the most of the rich insulation state information contained in PD, we propose a novel multi-information ensemble learning for PD pattern recognition. First, the ultra-high frequency and ultrasonic data of PD under four typical defects of GIS are obtained through experiment. Then the deep residual convolution neural network is used to automatically extract discriminative features. Finally, multi-information ensemble learning is used to classify PD types at the decision level, which can complement the shortcomings of the independent recognition of the two types of feature information and has higher accuracy and reliability. Experiments show that the accuracy of the proposed method can reach 97.500%, which greatly improves the diagnosis accuracy of various insulation defects.

PD Flexible Built-In High-Sensitivity Elliptical Monopole Antenna Sensor.

In view of the insufficient signal detection sensitivity of Gas Insulated Switchgear (GIS), partial discharge (PD), ultra-high frequency (UHF), and failure to conform with GIS surface structure when the existing rigid stereo structure UHF sensor is built in, this paper, using rectangular patch antenna equivalent technique, trapezoidal ground plane technique, and coplanar waveguide (CPW) feed line index asymptotic linearization technique, conducts research on a flexible built-in high-sensitivity elliptic monopole antenna. The flexible antenna, with a thickness of only 0.28 mm, can be kept at a voltage standing wave ratio (VSWR) of less than three in the 300 MHz to 3 GHz band under the curvature radius of 0, 100, 300, and 500 mm, and at less than two in the 650 MHz to 3 GHz band. Through the true 220 kV-GIS partial discharge experimental platform built to analyze the high frequency electromagnetic wave detection performance of the built-in flexible antenna, it is shown that the flexible built-in high-sensitivity elliptical monopole antenna designed in this paper can effectively detect the characteristic signals of high-frequency electromagnetic waves emitted by partial discharges with an average discharge amount below 10 pC.

A novel federated deep learning framework for diagnosis of partial discharge in gas-insulated switchgear

In recent years, many different deep learning methods have been developed to ensure the safe and stable operation of gas-insulated switchgear (GIS). However, the use of these methods to achieve excellent results depends on obtaining as much training data as possible, which is difficult to accomplish because of conflicts of interest among different clients and privacy concerns. To address this issue, this paper proposes a novel federated deep learning (FDL) for the diagnosis of partial discharge (PD) in GIS. A federated learning (FL) based on an improved federated averaging algorithm is proposed, which allows different clients to collaboratively participate in model training and preserves data privacy. In addition, a novel subtractive attention Siamese network is introduced for feature extraction and classification, which achieves the high-precision classification of unbalanced data. Experimental results showed that the diagnostic accuracy of the proposed FDL reached 95.61%, which was significantly higher than that achieved by other methods. The proposed FDL can also achieve excellent performance in the case of unbalanced samples and small samples. As a distributed learning, FL does not require clients to share data, and clients can collaboratively develop an effective global diagnostic model, which provides a novel solution for GIS PD diagnosis.

As-Doped h-BN Monolayer: A High Sensitivity and Short Recovery Time SF6 Decomposition Gas Sensor.

SF6 is a common insulating medium of gas-insulated switchgear (GIS). However, it is inevitable that SF6 will be decomposed due to partial discharge (PD) in GIS, which will cause hidden dangers to the safe and stable operation of equipment. Based on the DFT method, the two-dimensional nano-composite As-doped h-BN (As-BN) monolayer was proposed. By modeling and calculating, the ability of an As-BN monolayer as a specific sensor for SO2F2 (compared with an H2O adsorption system and CO2 adsorption system) was evaluated by parameters such as the binding energy (Eb), adsorption energy (Eads), transfer charge (ΔQ), geometric structure parameters, the total density of states (TDOS), band structure, charge difference density (CDD), electron localization function (ELF), sensitivity (S), and recovery time (τ). The results showed that an As-BN monolayer showed strong adsorption specificity, high sensitivity, and short recovery time for SO2F2 gas molecules. Therefore, the As-BN monolayer sensor has great application potential in the detection of SF6 decomposition gases.

Partial Discharge Diagnosis in GIS based on Pulse Sequence Features and Optimized Machine Learning Classification Techniques

Partial discharge (PD) diagnostics in Gas Insulated Switchgear (GIS) is important for reliable and secure operation of electrical utilities. Different techniques were used for PD diagnosis in GIS. In this work, PD diagnosis in GIS is proposed based on PD pulse sequence. PD pulse sequence only requires the measurement of PD phase appearance and its corresponding instantaneous voltage. The PD diagnosis of various defect types is implemented using five optimized machine learning classification techniques: decision tree classification, ensemble methods, k-nearest neighbouring, Discriminant analysis, and Naïve Bayes classification. The features used for PD pulse sequence are the voltage change and phase angle change between successive PD pulses. Three scenarios are proposed for predicting the defect types in GIS. The first scenario is built based on the extracted features for two successive PD pulses, the second scenario is built based on the extracted features for three successive PD pulses, while the last scenario is built based on the extracted features for four successive PD pulses. The results illustrate the superior detecting accuracy of the second scenario with the proposed five ML classification techniques. The optimized ML classification techniques are implemented and carried out based on MATLAB software package. The ensemble classification method exhibited the highest accuracy for PD-based diagnosis in GIS with an overall accuracy of 97.1%.

Numerical study on temperature rise and structure optimization for a three-phase gas insulated switchgear busbar chamber

In order to improve the simulation accuracy of the temperature rise, reduce the operating temperature, and improve the insulation performance of the gas insulated switchgear (GIS) busbar, this paper numerically studied a 252 kV three-phase GIS busbar chamber based on multiphysics coupling method. Various factors affecting busbar electromagnetic loss are analyzed, and the busbar structure is optimized combined with the Taguchi method. Firstly, the loss computational results show that, both skin effect and proximity effect change the conductor current distribution, the skin effect increases the GIS loss by 18.59 W/m (12.2%), and the proximity effect has little effect on the loss. The additional heat loss caused by conductor temperature rise cannot be ignored (13.2%). Secondly, Taguchi performance statistics show that, the conductor thickness (δ) and center distance (d0) have the greatest impact on the maximum temperature and minimum gas breakdown margin of GIS busbar, respectively, with the contribution rate of 80.2% and 65.1%, respectively. Finally, the optimal design parameter combinations with lowest operating temperature and best insulation performance for GIS busbar chamber are obtained with Taguchi method, respectively.

Experimental Evaluations on Seismic Performances of Porcelain and GFRP Composite UHV GIS Bushings.

To evaluate the seismic performances of the ultra-high voltage (UHV) gas-insulated switchgear (GIS) bushings made by porcelain and glass fiber reinforced polymer (GFRP) composite materials, shaking table tests were conducted on the two full-scale GIS bushings. The dynamic characteristics and seismic responses of the two UHV GIS bushings were obtained. The experimental results indicated that the two UHV GIS bushings meet the seismic requirements in the corresponding standards. The supporting frame and bus canister amplify the seismic responses of the UHV GIS bushings. Under earthquakes, the GFRP composite UHV GIS bushing is safer than the porcelain bushing. In the seismic design of the electrical substation, the large seismic displacement of the GFRP composite UHV GIS bushings should be considered.

Simulation and Measurement of Pressure Rise in GIS 145 kV due to Internal Arcing

Internal arc testing of metal-enclosed, SF6 gas insulated switchgear (GIS) is defined by IEC 62271-203 and is not a part of mandatory type tests.&#x0D; However, due to the increasing demands on the safety of personnel, more often the implementation of this test is required in the tender documentation. According to IEC, the duration of the electric arc is related to the performance of the protective system determined by the first and second stage of protection. For the rated short-circuit current equal or higher than 40 kA, during the first stage of protection (0.1 s), no external effects on enclosure other than the operation of pressure relief device is permitted. During the second stage of protection (≤0.3 s) no fragmentation is permitted, but burn-through is acceptable. The test should be carried out on the GIS compartment with the smallest volume at nominal gas pressure. Since a newly developed GIS 145 kV is designed as a three-phase encapsulated, arc initiation is achieved by short connecting of all three phase conductors in the vicinity of a partition by means of a thin metal wire. This ensures that two electric arcs burn simultaneously commutating between the phases, so the possibility of enclosure burn-through in this type of GIS is minimized. In order to prevent the release of SF6 gas in the atmosphere during the testing, a test enclosure should be placed in a protective gastight enclosure filled with air or more often SF6 gas at pressure of 0.1 MPa. This test object configuration significantly complicates the pressure rise calculation and increases the testing cost. In order to prevent enclosure fragmentation, the pressure difference between the test enclosure and the protective enclosure during the test should always be less than the bursting pressure of test enclosure. Also, the protective enclosure should be designed to withstand the maximum pressure rise that may occur after pressure relief device opens. In order to assess the likelihood of passing the upcoming type test for newly developed GIS, a computer program for calculation of pressure and temperature in the test enclosure and protective enclosure was developed. The mathematical model is based on the paper of the working group CIGRE A3.24, published in 2014. The basic model shown in the paper is enhanced by the real properties of the SF6 gas/plasma, evaporation of the electrode material and the insulator ablation. The contribution of exothermic/endothermic reactions between the gas and the electrode material on the pressure and temperature rise was also considered. At the same time, the measurements of pressure rise in GIS enclosure and protective enclosure were carried out in Končar High Power Laboratory. The experiments performed on a copper and aluminum electrodes in SF6 gas confirmed significantly higher contribution of aluminum electrodes to the pressure and temperature rise compared to the copper electrodes. The computer program is verified by measurement results.

Anomaly Detection of Gas-Insulated Switchgear (GIS) Equipment Based on Embedding Position Variational Auto-Encoder

Gas-insulated switchgear (GIS) is one of the most important power devices in the power system. Once a fault occurs, it is difficult to determine and locate the fault due to its fully enclosed nature, which may lead to greater accidents and huge losses. Many GIS anomaly detection methods based on vibration and deep learning (DL) have been developed in recent years. However, these DL-based methods only utilize one single sampling point data on GIS, while not all faults are reflected at the points currently in use due to the signal attenuation. In this paper, an embedded position variational auto-encoder (EP-VAE) model for anomaly detection of GIS devices is presented, which can model multipoint vibration signals. We fuse the position information and the corresponding one dimension vibration signal to the two dimension grayscale images by Gram matrix. Then, the normal grayscale images are feed into the EP-VAE model, which is optimized by KL divergence and reconstruction error jointly. In the test phase, when one of these two losses is higher than the normal images, it is identified as an abnormal fault. We use the vibration data from the actual operation of GIS to carry out the anomaly detection experiments. The results show that our model EP-VAE has a detection accuracy of 93.12% for normal vibration grayscale images, and an abnormality detection accuracy of 87.94%.

DC Flashover Performance of GIS Spacers Fluorinated at Different Temperatures

In order to verify the effectiveness of direct fluorination in improving the dc flashover performance of gas insulated switchgear (GIS) actual spacers and explore the effect of fluorination conditions, 126-kV disk spacers were surface fluorinated in the laboratory at the different temperatures of 35 °C, 55 °C, or 80 °C. The evaluation of surface physicochemical characteristics and basic electrical properties of the fluorinated disk spacers are realized by using the epoxy samples of the same material and simultaneously fluorinated with the disk spacers. Flashover tests indicate that direct fluorination significantly increases the dc flashover voltage of the actual spacer in SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> gas and reduces the dispersion of flashover voltage, fluorination temperature has a significant effect on the enhancement of the flashover voltage, and the fluorination at higher temperatures can produce a larger improvement in the flashover performance. In addition, these fluorinated spacers can withstand heat treatment at 120 °C, with unchanged flashover performance. Infrared analysis and scanning electron microscopy observation show that the fluorination at different temperatures changes surface chemistry of the disk spacer substantially, forming the fluorinated layers with different thicknesses, compositions, and structures. Measurements of surface conductivity and surface potential decay consistently show that direct fluorination increases surface conduction of the spacer, and the fluorination at higher temperatures leads to a more significant increase in surface conduction. The improvement in dc flashover performance, caused by direct fluorination, is mainly attributed to the suppression of surface charge accumulation by the increase in surface conduction of the spacer.

Acoustic Method for Multiple Free Metallic Particle Recognition in GIS/GIL

Free metallic particles are the main factor inducing flashover incidents and threatening the safety of gas-insulated switchgear (GIS) and gas-insulated transmission line (GIL) devices. Acoustic diagnosis is widely used to detect free metal particles due to its high sensitivity, but it generally fails under multiple particle conditions because of the overlap of acoustic signals. This paper proposes a new method of acoustically recognizing multiple free metallic particles. The pattern of t-Normalized N that can be used to distinguish single/multi-particles is determined first. Second, each particles neighboring collision signals are extracted from raw data by the established physical relation between adjacent collision signals. Finally, the free metal spherical double-particles mass and density values are calculated as per the numerical connection between adjacent collision signals. The proposed method is validated in a scale concentric hemisphere platform. The results of this work may provide a workable reference for GIS free metal multi-particle monitoring.