Discharge In Gas Insulated Switchgear Research Articles

Online Detection of Hydrogen Fluoride under Corona Discharge in Gas-Insulated Switchgear Based on Photoacoustic Spectroscopy.

Internal discharge and overheating faults in sulfur hexafluoride (SF6) gas-insulated electrical equipment will generate a series of characteristic gas products. Hydrogen fluoride (HF) is one of the main decomposition gases under discharge failure. Because of its extremely corrosive nature, it can react with other materials in gas-insulated switchgear (GIS), resulting in a short existence time, so it needs to be detected online. Resonant gas photoacoustic spectroscopy has the advantage of high sensitivity, fast response, and no sample gas consumption, and can be used for the online detection of flowing gas. In this paper, a simulated GIS corona discharge experimental platform was built, and the HF generated in the discharge was detected online by gas photoacoustic spectroscopy. The absorption peak of HF molecule near 1312.59 nm was selected as the absorption spectral line, and a resonant photoacoustic cell was designed. To improve the detection sensitivity of HF, wavelength modulation and second-harmonic detection technology were used. The online monitoring of HF in the simulated GIS corona discharge fault was successfully realized. The experimental results show that the sensitivity of the designed photoacoustic spectroscopy detection system for HF is 0.445 μV/(μL/L), and the limit of detection (LOD) is 0.611 μL/L.

Gas Insulated Switchgear Partial Discharge Type Identification Based on Frequency Division Features and Improved Long Short-Term Memory Neural Networks

In order to ensure the safe and stable operation of gas insulated switchgear (GIS), a new method for identifying the type of partial discharge in GIS is proposed. First, the GIS partial discharge experimental platform produced four partial discharge signals. Second, the energy moments are calculated at each frequency to produce the frequency division features after the four partial discharge signals have been decomposed using variational modal decomposition (VMD). Finally, the type of GIS partial discharge is determined using the LSTM model enhanced by the quantum cuckoo search (QCS) algorithm. The experimental results show that the proposed method can successfully identify four types of partial discharge in GIS with an accuracy of 99%.

Recognition of partial discharge in GIS based on image feature fusion

<p>Partial discharge (PD) is a significant electrical fault in gas-insulated switchgear (GIS), with various types posing different risks to insulation. Accurate identification of PD types is essential for enhancing GIS management and ensuring the reliability of electrical grids. This study proposes a novel approach for PD identification in GIS integrating completed local binary pattern (CLBP) feature extraction, feature engineering, and an optimized support vector machine (SVM). PD faults were simulated in GIS and phase-resolved pulse sequence (PRPS) data for four different forms of PD were gathered. CLBP was used to extract image features, and then the support vector machine recursive feature elimination (SVM-RFE) algorithm was used to evaluate feature importance. Then, linear discriminant analysis (LDA) was used to fuse the selected features and reduce redundancy. The fused features were classified using a bald eagle search algorithm combined with differential evolution (IBES)-optimized SVM, achieving a recognition accuracy of 99.38%. The results indicate that the proposed method effectively distinguishes between different PD PRPS patterns in GIS.</p>

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.

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.

A DFT study on the adsorption properties of Ti3C2O2 MXene towards SF6 decomposition gases

Detecting the partial discharge in gas-insulated switchgear (GIS) equipment by detecting the type and concentration of SF6 characteristic decomposition products is very important and effective in ensuring the regular operation of GIS equipment. In this study, VTi-Ti3C2O2 (Ti-deficient Ti3C2O2) and TM-doped (TM = Ti, Pt, Au) α-type Ti3C2O2 were carried out as the gas-sensing materials. The gas adsorption structures of SO2, SOF2, and SO2F2 on the surfaces of TM-modified Ti3C2O2 were established. Different adsorption systems' adsorption energies, electron densities, state densities, and times for gas desorption were examined using DFT calculations. Following the analysis, physical adsorption of SO2, SOF2, and SO2F2 occurs on the surface of Pt-Ti3C2O2 and Au-Ti3C2O2, while chemical adsorption occurs on the surface of VTi-Ti3C2O2. As a result, it is challenging to remove the gas molecules from the surface of VTi-Ti3C2O2, while it is simple to remove them from Pt-Ti3C2O2 and Au-Ti3C2O2 surfaces. At the same time, compared with gas adsorption on the surface of pristine Ti3C2O2, TM-modified Ti3C2O2 can improve the adsorption performance of pristine Ti3C2O2 on the characteristic decomposition products of SF6 to a certain extent.

Collaborative Domain Adaptation Network for Partial Discharge Source Localization in Gas-Insulated Switchgear

Abnormal discharge in gas-insulated switchgear (GIS) is a key cause of insulation failure, and it is also an external manifestation of insulation defects. Sensitive discharge source localization is an important goal of GIS partial discharge (PD) monitoring. However, most existing GIS PD source localization methods rely on time-delay estimation, which not only requires a high-precision synchronous sampling device but also suffers from serious interference. To this end, a collaborative domain adaptation network (CDAN) is proposed for GIS PD source localization. First, a synchronous squeezing wavelet transform is introduced to filter out the noise, eliminating the influence of noise on GIS PD source localization. Then, one-dimensional attention convolutional neural network is constructed to ensure that discriminative temporal fine-grained information is extracted. Next, a CDAN is proposed to transfer the localization knowledge learned from the simulation to actual GIS and achieve high-precision localization through domain matching and alignment. The results demonstrate that the error of the CDAN proposed is only 19.87 cm, which is considerably better than that from other methods. This work provides a reference solution for GIS PD source localization.

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 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.

The Study on a New Method for Detecting Corona Discharge in Gas Insulated Switchgear

Partial discharge (PD) is the main cause of insulation deterioration and failure of gas-insulated switchgear (GIS). Corona discharge (CD) is a typical type of PD in gas-insulated switchgear. Accurate and sensitive measurement of CD in GIS is of great significance for theoretical research in the laboratory and fault prediction of GIS in the actual operation. A novel method for detecting CD in GIS is proposed in this article. A coaxial structure model for simulating GIS is established. The CD detecting sensor based on the proposed method is designed for this model. An artificial tip defect is designed on the internal high voltage conductor of the coaxial model to simulate CD in GIS. A 50 Hz ac power supply, a dc negative power supply, and a dc positive power supply are applied as excitations to generate the CD at the defect, respectively. The discharge pulse signal is detected by the proposed sensor. The results show that the proposed sensor can completely obtain the information of the CD pulse signal. The proposed method has the same anti-electromagnetic interference ability as the ultrahigh-frequency method and has a high sensitivity to CD in GIS.

Adsorption characteristics of H2S, SO2, SO2F2, SOF2, and N2 on NiO–MoSe2 monolayer for gas-sensing applications

Gas insulated switchgear (GIS) is an important equipment in the power transmission and distribution system, which commonly fills with SF6/N2 insulating gas mixture. Due to the partial discharge in GIS, SF6 gas is likely to ionize and react with impurities inside the GIS, and produces SO2, H2S, SO2F2, and SOF2 characteristic gases. This work was devoted to detect the partial discharge by exploring new gas-sensing materials for characteristic gases detection. First, four possible modification sites of NiO on MoSe2 surface were built and optimized, it was found that the B2 site modification was the most stable one. Then, the optimal adsorption structure was obtained by analyzing the adsorption structures of gases on NiO–MoSe2 with different initial positions. Finally, the adsorption mechanism and gas-sensing characteristics were studied based on the density of state theory and molecular orbital theory. The results show that NiO doping greatly enhances the surface activity of MoSe2 for gas adsorption. The order of adsorption ability is H2S > SO2 > SOF2 > N2 > SO2F2, and the moderate adsorption energy indicates that NiO–MoSe2 could be a suitable gas-sensing material, which lays a theoretical foundation for the design of NiO–MoSe2 sensors. This research provides a new method to ensure the stability of GIS operation.

Simulation Study on Propagation Characteristics of Optical Signal of Point Discharge in GIS

With the development of the power system, gas insulated switchgear (GIS) is a commonly used module in power system at present. However, in the process of design, manufacturing, transportation and installation, it is inevitable to have various failures, causing serious consequences. At present, partial discharge detection has become an effective way to judge whether failures exist in GIS, especially optical detection method which is developing rapidly. The process of partial discharge will be accompanied by the emission of light signals. By detecting the light signal emitted by the partial discharge, the occurrence of the partial discharge can be effectively detected. Therefore, the propagation characteristics of the optical signal generated by light in GIS will be studied in this paper, and study the relationship between the position of light and data by TracePro, providing a reference for the layout of optical sensors. It is of great significance to the effective detection of partial discharge.

Evolution mechanism of ultraviolet and electrical phenomena induced by protrusion discharge in GIS

Abstract To improve the sensitive and reliability of condition monitoring in gas insulated switchgear (GIS), discharge perception system applied to GIS insulators protrusion discharge based on single ultraviolet photon is built. Step method is used to inspire the discharges of different severity, based on which, the regression relationship of ultraviolet (UV) signal and electrical signal are studied, and the perception mechanism of UV signal in different severity of discharges is analyzed. The study indicates that, the development of protrusion discharge in GIS can be divided into four stages, and there is no positive correlation between the number of ultraviolet photons caused by discharge radiation and insulation deterioration level, but related to the diffusion, migration and recombination of charged particles, then UV optical method for quantitative monitoring of discharge in each stage is provided, which provide a new method for quantitative measurement of GIS insulator discharge.

A Novel Sensor Detection Method of Partial Discharge in GIS based on the UHF Electromagnetic Wave

UHF electromagnetic wave has been widely used in various PD measurements of GIS (gas insulated switchgear) because of its high sensitivity, strong anti-interference ability, large effective detection range, fault location and pattern recognition. In this paper, a new method of detecting partial discharge of pouring hole on GIS insulator is studied. The transmission characteristics of UHF electromagnetic wave are simulated by FDTD. Based on the computer simulation technology (CST), a dipole antenna UHF sensor is developed. Finally, the sensitivity and validity of the sensor are tested on the 110kvgis experimental platform. Compared with the conventional method, the sensitivity of PD detection in GIS is obviously improved.

Adsorption and gas-sensing properties of Pt2–GaNNTs for SF6 decomposition products

Partial electric discharge faults in gas-insulated switchgear (GIS) seriously affect the operational stability of power systems. In this study, H2S, SO2, SO2F2, and SOF2 were selected as characteristic SF6 decomposition products to characterize the partial electric discharge faults in a GIS. Pt cluster-modified gallium nitride nanotubes (Pt2–GaNNTs) were selected as the gas-sensing material to study their adsorption and gas-sensing performance for SF6 decomposition products H2S, SO2, SO2F2, and SOF2. Calculation results showed that Pt2 clusters presented stable doping structures on GaNNTs that greatly enhanced the surface activity for gas adsorption. The adsorption structures, adsorption energy, charge transfer, energy band, density of states, and molecular orbit were analyzed on the basis of density functional theory. Gas molecules formed stable structures on the surfaces of Pt2–GaNNTs through chemical adsorption. The order of adsorption ability was SO2 > H2S > SOF2 > SO2F2, and that of gas-sensing ability was SOF2 > SO2 > H2S > SO2F2. In this article, a new method to detect the partial electric discharge in GIS is proposed. The proposed method helps enhance the running stability of GIS.

Partial Discharge Detection and Discharge Type Identification of Gas Insulated Switchgear Based on Photoelectric Detection

Gas insulated switchgear (GIS) is a relatively common high-voltage substation equipment. Due to the internal complex structure and integrated switch, it is easy to cause insulation defects, burrs and partial discharge of metal particles, thereby reducing the insulation of equipment performance, which has attracted the attention of many researchers. In this study, aiming at the problem of partial discharge in GIS, a detection device of partial discharge based on photomultiplier instrument is proposed. In this device, Zemax software is used to simulate a front-end optical signal receiving module that enhances the photosensitive surface and enhances the optical signal aggregation effect. In terms of signal processing, the output voltage signal of the photomultiplier instrument is filtered, and the corresponding signal is extracted for amplification. The partial discharge detecting device designed in the experiment is used to judge the internal insulation defects of the GIS, and to discriminate the metal protrusion defects, the metal particle defects on the insulator surface, the free metal particle defects, and the air gap defects between the insulator and the high voltage conductor, respectively. The partial discharge signal ϕ-u-v spectra of different defect models are constructed. Through the image simulation of partial discharge signals corresponding to defects, it is verified that the detection device can distinguish the partial discharge caused by different defects, which is helpful to prevent the occurrence of malignant discharge accidents and maintain the stable operation of GIS equipment.

Study of comparison between Ultra-high Frequency (UHF) method and ultrasonic method on PD detection for GIS

GIS (gas insulated switchgear), is an important equipment in power system. Partial discharge plays an important role in detecting the insulation performance of GIS. UHF method and ultrasonic method frequently used in partial discharge (PD) detection for GIS. However, few studies have been conducted on comparison of this two methods. From the view point of safety, it is necessary to investigate UHF method and ultrasonic method for partial discharge in GIS. This paper presents study aimed at clarifying the effect of UHF method and ultrasonic method for partial discharge caused by free metal particles in GIS. Partial discharge tests were performed in laboratory simulated environment. Obtained results show the ability of anti-interference of signal detection and the accuracy of fault localization for UHF method and ultrasonic method. A new method based on UHF method and ultrasonic method of PD detection for GIS is proposed in order to greatly enhance the ability of anti-interference of signal detection and the accuracy of detection localization.

Study of New Method Combined Ultra-High Frequency (UHF) Method and Ultrasonic Method on PD Detection for GIS

GIS (gas insulated switchgear), is an important equipment in power system. Partial discharge plays an important role in detecting the insulation performance of GIS. UHF method and ultrasonic method frequently used in partial discharge (PD) detection for GIS. It is necessary to investigate UHF method and ultrasonic method for partial discharge in GIS. However, very few studies have been conducted on the method combined this two methods. From the view point of safety, a new method based on UHF method and ultrasonic method of PD detection for GIS is proposed in order to greatly enhance the ability of anti-interference of signal detection and the accuracy of fault localization. This paper presents study aimed at clarifying the effect of the new method combined UHF method and ultrasonic method. Partial discharge tests were performed in laboratory simulated environment. Obtained results show the ability of anti-interference of signal detection and the accuracy of fault localization for this new method combined UHF method and ultrasonic method.

Influence of trace O<inf>2</inf> on SF<inf>6</inf> decomposition characteristics under partial discharge based on oxygen isotope tracer

As an excellent electrical insulation and arc-interruption medium, sulfur hexafluoride (SF 6 ) has been widely used in gas-insulated switchgear (GIS). Insulation failures in GIS are usually evolved from the partial discharges which are induced by hidden defects, such as metal protrusions, free conducting particles, floating components. In the presence of discharges, SF 6 decomposes into various by-products. Accordingly, these decomposition by-products are often used to detect and identify partial discharges in GIS based on SF 6 decomposition characteristics. Oxygen (O 2 ) as an impurity plays an important role in the formation of SF 6 decomposition by-products. In this paper, the influence of O 2 on SF 6 decomposition characteristics under partial discharge has been investigated based on oxygen isotope tracer 18O 2 and 16O 2 . In our experiments, SF 6 isotopic decomposition by-products were induced, as severity degree of partial discharge or ratio of 18O 2 to 16O 2 varied. The isotopic decomposition by-products were measured by gas chromatograph mass spectrometer. Based on the results, the sequences of reactions that lead to SO 2 F 2 and SO 2 were described. It is shown that the formation of SO 2 F 2 depends not only on O 2 but also H 2 O, whereas the formation of SO 2 has little dependence on O 2 .

UHF Signal Processing and Pattern Recognition of Partial Discharge in Gas-Insulated Switchgear Using Chromatic Methodology.

The ultra-high frequency (UHF) method is widely used in insulation condition assessment. However, UHF signal processing algorithms are complicated and the size of the result is large, which hinders extracting features and recognizing partial discharge (PD) patterns. This article investigated the chromatic methodology that is novel in PD detection. The principle of chromatic methodologies in color science are introduced. The chromatic processing represents UHF signals sparsely. The UHF signals obtained from PD experiments were processed using chromatic methodology and characterized by three parameters in chromatic space (H, L, and S representing dominant wavelength, signal strength, and saturation, respectively). The features of the UHF signals were studied hierarchically. The results showed that the chromatic parameters were consistent with conventional frequency domain parameters. The global chromatic parameters can be used to distinguish UHF signals acquired by different sensors, and they reveal the propagation properties of the UHF signal in the L-shaped gas-insulated switchgear (GIS). Finally, typical PD defect patterns had been recognized by using novel chromatic parameters in an actual GIS tank and good performance of recognition was achieved.