Operation Of Gas Insulated Switchgear Research Articles

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.

Measurement of Multipath Transients in Secondary Cables Near a 500 kV Gas-Insulated Switchgear During Operations

Operation of SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> gas-insulated switchgear (GIS) produces very fast transient overvoltage, which may interfere with the secondary system through many paths. As more and more smart devices are placed near GIS, their anti-EMI capability becomes more and more important. In response to the above problems, this paper carried out on-site measurement on the EMI in the secondary system during the operation of a 500 kV GIS. The EMI in the cable connecting the cabinet near the GIS and the control center 60 m away was paid attention to. By simultaneously measuring the electric field in space, the current through the grounding lead of the GIS, the EMI in the cables with different grounding methods, and the transient ground potential difference between the cabinet and the control center, the effects of different interference paths such as the spatial electromagnetic coupling and grounding system were analyzed. Results show that, the GIS operation will not only generate a spatial electric field, but also induce a circulating current on its grounding leads, resulting in an apparent ground potential difference on the grounding grid. When the shielding of the cable is grounded at both ends, the EMI is mainly determined by the spatial electric field and current in the shielding. As for the cable with single end grounded, the core-shielding voltage contains stronger high frequency components than that when the cable is grounded with both ends. The ground potential difference is the main source of core-ground voltage below 3 MHz when the cable is grounded at single end. Above 3 MHz the space electric field is the main source.

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.

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%.

Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS

Accurately identifying the types of insulation defects inside a gas-insulated switchgear (GIS) is of great significance for guiding maintenance work as well as ensuring the safe and stable operation of GIS. By building a set of 220 kV high-voltage direct current (HVDC) GIS experiment platforms and manufacturing four different types of insulation defects (including multiple sizes and positions), 180,828 pulse current signals under multiple voltage levels are successfully measured. Then, the apparent discharge quantity and the discharge time, two inherent physical quantities unaffected by the experimental platform and measurement system, are obtained after the pulse current signal is denoised, according to which 70 statistical features are extracted. In this paper, a pattern recognition method based on generalized discriminant component analysis driven support vector machine (SVM) is detailed and the corresponding selection criterion of involved parameters is established. The results show that the newly proposed pattern recognition method greatly improves the recognition accuracy of fault diagnosis in comparison with 36 kinds of state-of-the-art dimensionality reduction algorithms and 44 kinds of state-of-the-art classifiers. This newly proposed method not only solves the difficulty that phase-resolved partial discharge (PRPD) cannot be applied under DC condition but also immensely facilitates the fault diagnosis of HVDC GIS.

First-Principle Study of Rh-Doped Nitrogen Vacancy Boron Nitride Monolayer for Scavenging and Detecting SF6 Decomposition Products.

The scavenging and detection of sulfur hexafluoride (SF6) decomposition products (SO2, H2S, SO2F2, SOF2) critically matters to the stable and safe operation of gas-insulated switchgear (GIS) equipment. In this paper, the Rh-doped nitrogen vacancy boron nitride monolayer (Rh-VNBN) is proposed as a gas scavenger and sensor for the above products. The computational processes are applied to investigate the configurations, adsorption and sensing processes, and electronic properties in the gas/Rh-VNBN systems based on the first-principle calculations. The binding energy (Eb) of the Rh-VNBN reaches −8.437 eV, while the adsorption energy (Ead) and band gap (BG) indicate that Rh-VNBN exhibits outstanding adsorption and sensing capabilities. The density of state (DOS) analysis further explains the mechanisms of adsorption and sensing, demonstrating the potential use of Rh-VNBN in sensors and scavengers of SF6 decomposition products. This study is meaningful as it explores new gas scavengers and sensors of SF6 decomposition products to allow the operational status assessment of GIS equipment.

Experimental research on the influence of temperature on the discharge signal of void defects in GIS

Temperature is an important environmental factor during the operation of gas-insulated switchgear (GIS), affecting the evaluation results of the GIS equipment to increase the risk of the power system. However, the influence of temperature on the partial discharge detection signal of GIS is still unclear. Aimed at the common void defects in GIS, the law of change on the number of ultrahigh-frequency (UHF) pulses, the UHF amplitude, the characteristic value of the UHF map, and the maximum apparent charge of a single pulse with temperature are obtained using the UHF method and IEC60270, and corresponding theoretical analysis is carried out. The results show that an increase in temperature leads to a decrease in the void discharge delay time, causing an increase in UHF pulses and a decrease in the apparent charge of a single discharge pulse in the experiment. The increase of temperature makes the void discharge current rise quickly so that the induced UHF amplitude increases. In the range of 40–70°C, the maximum pulse amplitude increases by approximately 30% for every 10°C increase, and the average pulse amplitude increases by approximately 12%. The result of UHF signals affected by temperature obtained in this study has research significance for the realisation of a comprehensive evaluation of the insulation state of GIS equipment considering temperature.

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.

GIS Mechanical Fault Diagnosis Method Based on Middle Time Mel Cepstrum Coefficient

Mechanical fault is a common fault of gas insulated switchgear (GIS). If not found in time, it will cause major safety hazards such as opening and closing fault. In this paper, a diagnosis method based on improved Mel cepstrum coefficient for GIS mechanical fault on-line monitoring is proposed. Firstly, the Mel frequency cepstral coefficients (MFCC) are extracted from the preprocessed sound signals; in order to adapt to the characteristics of smooth sound energy change in GIS operation, the MFCC is optimized to obtain improved features; Support Vector Machine (SVM) is introduced to build a GIS mechanical fault diagnosis model based on acoustics and uses bagging algorithm to integrate the SVM model. In this study, mechanical fault is simulated on real GIS to obtain real fault sound signals for training and testing. The experimental results show that compared with the traditional MFCC, the improved MFCC has a higher recognition accuracy in GIS fault sound recognition system. The F1-score is generally over 80%, and the F1-score of specific mechanisms can even reach 99%.

Theoretical Study of the Role of a Catalytic Water Molecule in the Hydrolysis of Thionyl Tetrafluoride (SOF4).

Sulfuryl fluoride (SO2F2) plays an important role in the operation of gas-insulated switchgear (GIS) equipment where it is widely used as the characteristic gas for discharge diagnostics. However, the formation mechanism of SO2F2 is currently unclear, and as a consequence, we have employed a range of ab initio methods to investigate the hydrolysis reaction of SOF4 that is known to afford SO2F2 in the gas phase. These results suggest that two H2O molecules are incorporated into a low energy transition state to afford an H-bond network that facilitates proton transfer during the hydrolysis of SOF4.

Ferroresonance Analysis of 500kV GIS Substation during Commissioning Process

Ferroresonance is an important factor affecting the security and stability operation of Gas Insulated Switchgear (GIS) substation. The nonlinearity of excitation characteristic of inductance elements with iron cores is the fundamental cause for exciting ferroresonance. A 500kV GIS substation is taken into account in this paper, a variety of operating conditions which may excite ferroresonance during the commissioning process are analyzed by a numerical simulation method. The comparison with actual commissioning results shows that ferroresonance will be excited by opening circuit breakers to switch off no-load buses during the commissioning process. For single-phase electromagnetic potential transformer (PT), ferroresonance can be effectively suppressed by connecting damping resistance in parallel with residual winding temporarily.

Vibration Feature Analysis for Gas-Insulated Switchgear Mechanical Fault Detection under Varying Current

As a key component to ensure the safe operation of the power grid, mechanical defect diagnosis technology of gas-insulated switchgear (GIS) during operation is often neglected. At present, GIS mechanical fault detection based on vibration information has not been developed. The main reason is that the excitation current is considerable but uncontrollable in the actual operation of GIS. It is difficult to eliminate the influence of excitation on the vibration amplitude and form an effective vibration feature description technology. Therefore, this paper proposes a unified feature-extraction method for GIS vibration information that reduces the influence of current amplitude for mechanical fault diagnosis. Starting from the GIS mechanical analysis, the periodicity of vibration excitation and the influence of amplitude are discussed. Then, combined with the non-linear characteristics of GIS systems and non-linear vibration theory, the multiplier frequency energy ratio (MFER) is designed to extract vibration-unified features of GIS for diagnosing the mechanical fault under different current levels. The diagnosis results of the experimental data with different feature-extraction methods show the applicability and superiority of the proposed method in the GIS’s mechanical fault-detection field based on vibration information.

Silicone Grease for Outdoor Gas-insulated Switchgear (GIS) Application

Silicone grease are widely applied in flange connections of gas-insulated switchgear (GIS) providing protection against flange and sealing O-ring corrosion and subsequent SF6 gas leakage. During GIS operation, grease bleeding phenomenon was frequently observed, which not only result in a poor appearance of flange surface but a potential SF6 leakage in the long-term application. In this paper, the possible reasons for this phenomenon is briefly discussed. In addition, the key parameters and outdoor performance of several commercial grease products were verified for a better understanding of its temperature resistance. It is shown that some grease products can have a better control on bleeding performance and should be more suitable for the outdoor GIS application.

Effects of non‐linear conductivity on charge trapping and de‐trapping behaviours in epoxy/SiC composites under DC stress

Gas-insulated switchgear (GIS) spacers are made of epoxy resin. However, the surface charge accumulation has been a great concern to the safe operation of GIS, which causes the frequent flashover faults on spacers. In this study, micro-silicon carbide (SiC) particles with non-linear conductivity were added into epoxy matrix and the filler content varied from 0 to 14 vol%. Then, the bulk conductivity and surface potential decay (SPD) tests were conducted. The obtained results showed that the epoxy/SiC composites have obvious non-linear conductivities and the non-linear-conductivity threshold decreases with the increasing filler content. The addition of SiC can effectively resist the rise of surface potential and enhance the surface charge dissipation process. From the trap energy distributions, it can be inferred that the deep traps of ~0.9 eV should be the intrinsic traps of epoxy and the shallow traps of ~0.8 eV are considered to be introduced by SiC. Furthermore, the simulation results confirmed that the sharp increase of carrier mobility in non-linear region significantly reduces the remaining time and possibility of a de-trapped charge being recaptured by traps before reaching the grounded electrode. Therefore, the high conductivity in non-linear region contributes a lot to the increase of SPD rate.

Investigation of frequency characteristics of typical PD and the propagation properties in GIS

The secure operation of Gas Insulated Switchgear (GIS) is crucial for the bulk power systems, since the failure of GIS apparatus would cause severe interruption of power transforming and power supply at substations. Therefore, the monitoring and maintenance schemes of GIS devices have gradually taken the reliability-based indices into account, of which Partial Discharge (PD) levels in online monitoring are of substantial significance because PD serves as a major cause of insulation break-down. Ultra High Frequency (UHF) method is becoming widely adopted as the major inspection approach for PD in GIS. This paper presents the experiment-based results of the frequency characteristics of UHF signals caused by PD in typical defects using entire GIS operating system platforms. Specifically, the results covered the propagation attenuation of the UHF signals with peak to peak value and cumulative energy through typical structures, including basin-type insulators, L-shaped branch, isolating switches, current transformers, etc. The results serve as substantial criteria for optimal band width selection for high frequency detection, and as notable concerns to improve the sensitivity of UHF detection and the efficiency of online monitoring by effectively locating the external sensors for PD UHF detection.

Propagation Properties of High-Frequency Electromagnetic Wave Through Typical In-Field GIS Structures

The safe operation of gas-insulated switchgears (GIS) is crucial for modern power systems, since the breakdown or failure of GIS equipment would cause severe interruption of the power supply and even accidents. Among the problems that would potentially harm the operation conditions of GIS apparatus, partial discharge (PD) and its subsequent electromagnetic (EM) waves serve as the essential considerations. In order to investigate the propagation property of high-frequency EM waves excited by PD in typical GIS structures, experiments and corresponding numerical simulation were conducted with convincing results presented in this paper. The spectrum analyzer working in two-port insertion loss mode was applied to capture the attenuation characteristic of the EM wave from 100 MHz to 1 GHz through three representative types of structures: the spacer, L-shaped branch, and T-shaped branch. Besides, the corresponding simulation platforms have been built and the High Frequency Structure Simulator was adopted to numerically simulate the propagation of high-frequency EM wave in GIS. The simulation results follow substantial consistency with the experimental results. Based upon these results, major causes of the EM wave attenuation have been analyzed, namely, the reflection and the mode transformation. Taking the presented EM wave propagation properties into account, the optimal placement of PD ultra-high frequency sensors for online monitoring of GIS equipment is discussed to improve the monitoring sensitivity and efficiency.

Recognition of partial discharge waveshape patterns for gas insulated switchgear

Early warning system based on the detection of UHF SF6 partial discharge (PD) signals is a necessary means for the protection of Gas-Insulated Switchgear (GIS) in service as well as the power system to which it is connected. In order to ensure the safe and reliable operation of GIS, it is important to adopt an effective diagnosing method, which is able to identify signals of harmful defects promptly. Unlike approaches such as Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) based techniques, a novel approach to extract discriminative features directly from time-domain UHF signals is introduced in this paper. With the proposed approach, UHF signal waveforms of different sources are partitioned and quantified so that the harmful (SF6 PD) and not-so-harmful (air corona) UHF signals are classified promptly. The investigation is based on the experiment data measured from a 300 KV GIS with encouraging results, which proves the usefulness of this proposed approach.