Gas Insulated Switchgear Equipment Research Articles

Investigating the impact of X-ray irradiation on the SF6 gas discharge in GIS equipment

ABSTRACT Gas Insulated Switchgear (GIS) is extensively utilized in power grid systems. However, the existence of numerous photoelectrons in the SF6 gas during X-ray non-destructive testing in ultra-high-voltage (UHV) GIS equipment may induce gas streamer discharge in a strong electric field. To address this issue, in this work, the impact of X-ray irradiation on the SF6 gas discharge in GIS equipment was systematically analyzed. Firstly, one GIS engineering mode with a three-dimensional electric field was established, and a physical GIS model was established by the Geant4 and Garfield++ codes. Secondly, the electrons’ ionization, and multiplication processes in SF6 gas during the X-ray irradiation were studied. Finally, a test platform with an equivalent electric field tube, which had the same intensity as in the GIS equipment, was established to verify the physical GIS model. X-ray beams with 0.1 MeV, 0.16 MeV, 0.3 MeV, and 1.25 MeV energy were used during the test process. From our analysis, it was concluded that X-ray irradiation can cause an electron avalanche in SF6 gas and the intensity of the avalanche is low, which is far away from the appearance of the streamer discharge.

An All-Fiber FLRD System for SO2 Detection Based on Graphene-Coated Microfiber

The accurate and effective detection of SF6 decomposition components inside a gas-insulated switchgear (GIS) is crucial for equipment fault diagnosis and condition assessment. The current method for detecting SF6 decomposition components involves gas extraction at the GIS inlet, which only provides limited information on the decomposition component content. Therefore, there is a need to explore more effective ways to obtain internal gas component information within GIS. In this study, we propose a graphene-coated microfiber gas detection method for SO2. We establish a physical simulation model of the microfiber and analyze the sensing mechanism of the microfiber diameter and cladding refractive index changes in its evanescent field. A graphene-coated microfiber gas sensor was prepared using a drop-coating method, and a fiber loop ring-down (FLRD) gas detection system was constructed for the experimental studies on SO2 gas detection. The results demonstrated that the graphene-coated microfiber exhibits an excellent gas-sensitive response to SO2 and achieves trace-level detection at room temperature. The concentration range of 0 to 200 ppm showed good linearity, with a maximum detection error of 4.76% and a sensitivity of 1.24 ns/ppm for SO2. This study introduces an all-fiber method for detecting SF6 decomposition components, offering a new approach for online monitoring of SF6 decomposition components in GIS equipment using built-in fiber-optic sensors.

Partial Discharge Pattern Recognition Method Based on Transfer Learning and DenseNet Model

With the development of intelligent sensing technology, a large amount of partial discharge (PD) time-domain waveform images are generated in the on-site detection of gas insulated switchgear (GIS) partial discharge. Traditional pattern recognition methods are mostly aimed at structured data and cannot directly identify defect types of such data.At the same time, the deep learning method for GIS PD pattern recognition is generally faced with the problem of small samples.In order to solve the above problems, this paper proposes a PD pattern recognition method based on transfer learning and DenseNet model.Firstly, the time-domain waveform images are processed by image enhancement, normalization, image compression and other image processing techniques.The FDTD method was used to simulate GIS PD, and the time domain waveform image database of four PD defects are established.Using CNN and transfer learning, the recognition accuracy of the model is increased to 95%, with better robustness.The recognition performance of different CNN structures is studied. The results show that DenseNet model has higher accuracy than other structures and shorter training time. This study can be used to diagnose the insulation status of GIS equipment in-site.

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.

Carbon Emission Evaluation Method and Comparison Study of Transformer Substations Using Different Data Sources

The construction of transformer substations in transmission lines is a systematic, technical, and complex project with the need for numerous materials and resources. Under the development of the green economy, the requirements for energy conservation and carbon reduction have improved; hence, an assessment of carbon emissions in transformer substations is urgently needed. A calculation method was proposed in the present study to analyze the carbon emissions of transformer substations with different kinds of data sources, which were collected from several practical projects in the west-to-east power transmission project. In this study, a detailed comparison and discussion regarding the differences in carbon emissions of 750 kV transformer substations caused by hydrology, geology, engineering quantity, and other factors were conducted. The mean value, standard deviation, and 90% confidence interval of carbon emissions were obtained by Monte Carlo simulation through MATLAB. Results show that the total carbon emissions of the selected 750 kV transformer substations are between [56,000, 68,000] t CO2 eq. Construction engineering accounts for more than 50% of carbon emissions, followed by installation engineering and additional services. In terms of input items, electricity distribution buildings contribute more than 39% of total carbon emissions, followed by cable/earthing systems, which account for 14% of total carbon emissions. Gas insulated switchgear (GIS) and air insulated switchgear (AIS) could adopt different types of equipment foundations, and GIS equipment foundations would generate fewer carbon emissions due to the smaller land area and input materials. This study can provide experience and reference for similar projects and further guide the substation carbon emission reduction work.

Rethinking unsupervised texture defect detection using PCA

In recent times, deep learning frameworks have become the preferred solution for visual object detection tasks, including texture defect detection. However, in real-world industrial applications, such as operation and maintenance of gas-insulated switchgear (GIS) equipment, collecting a large number of samples in advance is difficult because opening GIS equipment affects its normal operation; moreover, only four real-world GIS equipment defect samples are available currently. Most deep learning methods perform poorly when trained using a small number of labelled samples or when there is a lack of computational resources. In addition, the features extracted by multi-layer convolution and down-sampling are often unsuitable for small-size and low-contrast defects. However, the structure of the repetitive textured surface is simple and easy to model. Hence, this paper proposes a texture-defect detection method using principal components analysis (PCA) and histogram-based outlier score (HBOS) that requires only a small number of unlabelled samples and low computational complexity. First, PCA is utilized to extract features from the training patches to enhance the difference between the defect and background regions. Then, non-saliency suppression is applied for feature fusion to obtain the saliency map. Finally, the saliency value is converted to the defect score, and the defect is located by HBOS. Experiments on two public datasets and a GIS simulation equipment demonstrate that the proposed method is suitable for product surface defect detection and operation and maintenance in power distribution equipment. Moreover, compared with traditional texture feature-based and deep learning methods, the proposed method yields better results for small-size and low-contrast defects with only four unlabelled training samples per class.

Time-Optimal Trajectory Planning of Flexible Manipulator Moving along Multi-Constraint Continuous Path and Avoiding Obstacles

To solve the trajectory planning problem of the flexible manipulator under various constraints such as end-camera attitude, drive space, and obstacles during video inspection along a continuous path in narrow three-dimensional space, this paper proposes a time-optimal trajectory planning method from the initial configuration to the final configuration. The trajectory planning problem is transformed into a multi-constraint optimization problem. First, to realize continuous video inspection in an unstructured complex environment, by analyzing the geometric model of the two-segment flexible manipulator with a camera at the end, the pose constraints between the camera and the shooting surface are formulated by the space vector method, the driving constraints are formulated based on kinematics, and the obstacle constraints are formulated by space mapping. Then, a multi-constraint optimization model is constructed to generate the smooth trajectory of the drive cable of the flexible manipulator by minimizing the total time of continuous path motion. Compared with the conventional point-to-point collision avoidance planning solution method, this paper starts from the global perspective and investigates the less considered continuous path trajectory planning problem; also, the swarm intelligence algorithm artificial jellyfish search algorithm (JS) is employed to optimize the solution and find the minimum time trajectory conforming to a variety of complex constraints. Finally, a simulation is conducted with CoppeliaSim, and the continuous path video inspection experiment is carried out in the 500KV GIS (Gas Insulated Switchgear) equipment, the simulation and experimental results indicate that the planned drive cable trajectory is smooth and effective. In addition, each path point is tracked and obstacles are avoided safely. When the flexible manipulator moves along the whole path, the pose of the camera satisfies the relaxed attitude constrain The proposed method can guide the two-segment flexible manipulator to complete the continuous video inspection task of the GIS cavity wall and conductive column surface.

MCTSR: A super‐resolution method for defects in gas‐insulated switchgear X‐ray images based on multi‐scale contextual transformers

AbstractAs the core equipment of transmission and distribution hubs, the operational status of gas‐insulated switchgear (GIS) is closely linked to the safety of the power system. Recently, X‐ray digital imaging technology has been extensively used in GIS equipment fault detection. However, the X‐ray image of GIS is blurred, which is not conducive to the detection of tiny defects. Thus, a super‐resolution method for GIS X‐ray images based on multi‐scale context transformers is proposed in this study, namely MCTSR. Firstly, a second‐order image degradation model is introduced to generate GIS X‐ray low‐resolution images that more closely resemble the real world. Secondly, a contextual transformer gate module is constructed to improve attention to tiny defects in GIS X‐ray images. Thirdly, a U‐Net discriminator network based on multi‐scale contextual transformers is intended to enrich the information of the generated images. Finally, the proposed discriminator is combined with the existing generator to compose a super‐resolution method applicable to GIS X‐ray images. The experimental results demonstrate that the method outperforms other methods in peak signal‐to‐noise ratio and structural similarity on the constructed GIS X‐ray image dataset. In addition, the output image of the proposed method facilitates the subsequent defect detection.

A novel method for small and unbalanced sample pattern recognition of gas insulated switchgear partial discharge using an auxiliary classifier generative adversarial network

AbstractConvolutional neural networks (CNNs) have been widely used for gas‐insulated switchgear (GIS) partial discharge (PD) pattern recognition due to their powerful feature extraction ability. However, there is commonly a scarcity of fault samples due to low insulation failure rate of GIS equipment, which degrades the diagnostic performance of these CNN networks when directly applied to small and unbalanced datasets. Therefore, we propose a novel auxiliary classifier generative adversarial network for GIS PD pattern recognition for small and unbalanced samples. First, we propose using synchrosqueezed wavelet transform to extract time‐frequency characteristics of PD pulses and obtain a time‐frequency image with high energy aggregation and time‐frequency distribution rate. Then, we propose an improved generative adversarial network with an auxiliary classier and self‐attention mechanism, which can generate high‐quality PD samples for situations with few classes. Experiments show that our proposed method can reach 95.75% recognition accuracy for small datasets, which is the highest among several comparable methods. Furthermore, the proposed method has excellent and stable recognition performance for various unbalanced datasets.

Study on Influencing Factors of Temperature Variation Displacement Compensation Behavior of Gas Insulated Switchgear Shell Bellows

Bellows are the key structures that compensate for and absorb the temperature-dependent displacement of a gas insulated switchgear (GIS) shell. It is of great engineering value to master the relationship between the temperature-dependent displacement behavior and various influencing factors. Based on the analysis of the influencing factors of the temperature variation displacement compensation ability of the GIS bellows, a bellows model was established. By coupling it with a shell having different degrees of bending through two layout methods, finite element simulation was carried out based on ABAQUS software to obtain the regular relationship between the temperature variation displacement compensation behavior of the bellows and various influencing factors. The results of the case study show that the temperature change displacement compensation ability of the bellows is most significantly affected by their own structural size. Reducing the wall thickness and increasing the wave height of the bellows can effectively improve the temperature change displacement compensation ability of the bellows and reduce the stress concentration in the compensation process. The change in the GIS shell shape is the second-most important influence; when the sliding support base of the bus barrel is lower than the fixed support base, the amount of temperature change displacement compensation of the bellows increases and the compensation capacity decreases, and, on the contrary, the temperature compensation capacity increases. Within the operating temperature range, the compensation ability of bellows with a distributed arrangement is better than that of a centralized arrangement, and can alleviate the initial deformation caused by gravity and gas pressure in GIS equipment.

Operation space analysis and trajectory planning of mechanical arm in narrow space for GIS (gas insulated switchgear) inspection robot

Purpose The purpose of this paper is to achieve optimal climbing control of the gas-insulated switchgear (GIS) robot, as the authors know that the GIS inspection robot is a kind of artificial intelligent mobile equipment which auxiliary or even substitute human labor drive on the inner wall of the gas-insulated metal enclosed switchgear. The GIS equipment fault inspection and maintenance can be realized through the robot manipulator on the mobile platform and the camera carried on the fuselage, and it is a kind of intelligent equipment for operation. To realize the inspection and operation of the GIS equipment pipeline without blind spots, the robot is required to be able to travel on any wall inside the pipeline, especially the top of the pipeline and both right and left sides of the pipeline, which requires the flexible climbing of the GIS inspection robot. The robot device has a certain adsorption function to ensure that the robot is fully attached to the wall surface. At the same time, the robot manipulator can be used for collision-free obstacle avoidance operation planning in the narrow operation space inside the GIS equipment. Design/methodology/approach The above two technologies are the key that the robot completes the GIS equipment inspections. Based on this, this paper focuses on modeling and analysis of the chassis adsorption characteristics for the GIS inspection robot. At the same time, the Denavit Hartenberg (D-H) coordinate model of the robot arm system has been established, and the kinematics forward and inverse solutions of the robot manipulator system have been derived. Findings The reachable working space point cloud diagram of the robot manipulator in MATLAB has been obtained based on the kinematics analysis, and the operation trajectory planning of the robot manipulator using the robot toolbox has been obtained. The simulation results show that the robot manipulator system can realize the movement without collision and obstacle avoidance. The space can cover the entire GIS pipeline so as to achieve no blind area operation. Originality/value Finally, the GIS inspection robot physical prototype system has been developed through system integration design, and the inspection, maintenance operation experiment has been carried out in the actual GIS equipment. The entire robot system can complete the GIS equipment inspection operation soundly and improve the operation efficiency. The research in this paper has important theoretical significance and practical application value for the optimization design and practical research of the GIS inspection robot system.

Novel GIS Mechanical Defect Simulation and Detection Method Based on Large Current Excitation With Variable Frequency

The mechanical defects of gas-insulated switchgear (GIS) equipments seriously threaten their safe and stable operation, but there are detection problems such as latency and weak vibration response, especially for high-capacity GIS with large size. In this paper, a novel GIS mechanical defect simulation and detection method based on large current excitation with variable frequency is proposed to solve this problem. First, a 500 kV GIS mechanical defect simulation and detection system with variable frequency and large current loading capacity was designed by structural parameter adjustment, power matching and harmonic suppression based on the nonlinear vibration mechanism. Then, the vibration characteristics of normal and typical defects of GIS equipment with different excitation frequencies were experimentally studied. Finally, the evolution laws of vibration signals and features for mechanical defects with different severity levels at different currents were explored, and a defect identification model for different load intervals was established. Results show that the amplitude growth and superharmonic point frequencies of vibration signals are related to the defect types and excitation current frequencies. The nonlinear vibration signal features such as kurtosis and gravity center frequency of defective equipment are more significant when using the proposed detection method compared with single frequency excitation, especially at large currents above 3,000 A, which not only makes it easier to find internal defects at an early stage but also helps to overcome the missing detection problem of the traditional method, and the defect recognition accuracy based on vibration signals of variable frequency excitation is higher.

Contact Failure Diagnosis for GIS Plug-In Connector by Magnetic Field Measurements and Deep Neural Network Classifiers

This article presents a contact fault diagnosis method of gas-insulated switchgear (GIS) plug-in connector via magnetic field measurement, magnetic field visualization, and deep neural network (DNN) classifiers. First, the surrounding magnetic field of GIS plug-in connector with normal contact (NC) condition and with artificially designed contact failures was measured by the Hall sensor array. Then, the measured magnetic field was gathered with an original matrix of 16 <inline-formula> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> 16 dimensions. The <inline-formula> <tex-math notation="LaTeX">$\vert$</tex-math> </inline-formula>original matrix was then visualized by the max&#x2013;min normalization and correlation matrix. Database containing 11 000 magnetic field images was labeled and segmented as training, validation, and test datasets. Furthermore, high-dimensional features of input magnetic field images were extracted by different DNN filters, including convolutional neural network (CNN), simple recurrent neural network (Sim-RNN), and long short-term memory (LSTM) network. Then, extracted high-dimensional features were fed into a fully connected (Fc) neural network with SoftMax classifiers to identify different contact faults. Finally, the performance of different DNN-based classifiers is compared by the fault classification merits, <inline-formula> <tex-math notation="LaTeX">$t$</tex-math> </inline-formula>-distributed stochastic neighbor embedding (<inline-formula> <tex-math notation="LaTeX">$t$</tex-math> </inline-formula>-SNE) feature clustering, and confusion matrixes. Results show that the DNN-based model could achieve contact fault classification task with an accuracy of 97.7% and <inline-formula> <tex-math notation="LaTeX">$F_{1\_\mathrm{score}}$</tex-math> </inline-formula> of 0.985. Therefore, the proposed method is useful for designing a high-performance contact status monitoring system of GIS equipment, thus improving its operation safety.

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.

Surface flashover patterns of GIS basin insulator under impulse voltage

Basin insulator is an important component of SF6 gas insulated switchgear (GIS), and its fault is also one of the major causes of GIS equipment failure. Due to the large capacity of high voltage power system, the faulty basin insulators are generally burned severely by the high energy arc, so the fault diagnosis is very difficult. In this paper, the surface flashover pictures of several full-scaled GIS basin insulators under impulse voltage are collected, and the morphology features of flashover traces initiating from different positions are summarized. As a result, the surface flashover patterns are divided into three types: 1) surface of shielding electrode initiated flashover, 2) triple junction initiated flashover and 3) surface of insulator initiated flashover. The inception and development process of flashover are also summarized. Besides, the diagnosis method of discharge inception position is proposed. Firstly, check the ablation points at high voltage terminal. If there is a ablation point on the surface of shielding electrode, the flashover initiates from there. If there is only ablation point at the triple junction, the inception position depends on the flashover trace morphology: when the flashover trace disperses in a single direction towards grounding electrode or high voltage electrode, the flashover initiates from the opposite triple junction; when the flashover trace disperses towards both electrodes, the flashover initiates from the insulator surface. The dispersion direction could be directly distinguished from the black carbonized dendritic traces, or from the “white dendritic traces” with the auxiliary methods. The conclusions could help the substation maintenance staff do the root cause analysis of the surface flashover, and assist the researchers in the structural optimization design of basin insulator.

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.

Research on Mechanical Defect Detection and Diagnosis Method for GIS Equipment Based on Vibration Signal

Gas insulated switchgear equipment (GIS) is widely used in power system, and more attention has been paid to discharge defects than mechanical defects. However, since mechanical defects are a major cause of the failure in GIS, it is of great significance to carry out relevant research on mechanical defects. Detection and diagnosis methods of mechanical defects based on vibration signal are studied in this paper. Firstly, vibration mechanisms of GIS are analyzed. Due to structural differences between single phase insulated type GIS and three phase insulated type GIS, there are big differences in vibration mechanisms between the two types of GISs. Secondly, experimental research on mechanical defects is carried out based on a 110 kV GIS equipment and a self-developed vibration detection system; results show that mechanical defects can be diagnosed by analyzing signal amplitude, frequency spectrum and waveform distortion rate, and a large current is more beneficial for diagnosing mechanical defects. Lastly, field application has been carried out on 220 kV GIS equipment, and a poor contact defect is found, demonstrating that abnormal diagnosis can be realized by method proposed in this paper. Experimental research and field application demonstrate the feasibility and effectiveness of detection and diagnosis method for mechanical defects based on vibration signal and provide experience for subsequent engineering application.

LBM simulation of heat transfer processes inside GIS capsule filled with different insulation gas mixtures

Lattice Boltzmann method (LBM) scheme is adopted to simulate the heat transfer processes inside isolated phase and three-phase enclosure gas insulated switchgear (GIS) capsules. D2Q9 fluid flow and thermal Lattice Boltzmann equations (LBEs) are solved through streaming and collision processes. Buoyancy force driven by natural convection is considered by the Boussinesq model. Power losses and thermal boundary conditions are obtained from closed formulas. Numerical results by LBM simulations are compared with those from physical experiments and conventional CFD simulations. Results show that LBM scheme could provide a simple and efficient alternative tool in the flow-thermal simulation of GIS equipment with desirable accuracy. Thermal performances of GIS capsule filled with different insulation gas mixtures (SF6, N2, CF4, CO2, CF3I) are then analyzed. Results show that upper conductors of three-phase GIS has larger temperature rise than lower conductor due to the natural convection effect. Conductor temperature rise inside gas mixture increases with the increase of second gas (N2, CO2, CF4) fraction. The thermal performance of GIS capsule could be reduced with the presentation of insulation gas mixtures.

System Optimization and Robustness Stability Control for GIS Inspection Robot in Complex Microgrid Networks

GIS (gas-insulated switchgear) is important equipment in the substation system in a complex microgrid network. Due to the long-term service in harsh operation environments, the electrical performance of GIS equipment will be seriously affected. Therefore, the regular maintenance of GIS equipment in the substation system is a routine task so as to ensure the normal operation of the microgrid networks. The traditional method relies on manual labor, where not only with the low operation efficiency but also regarding some maintenance, labors cannot reach the inside of the GIS equipment, which seriously restricted the completion of GIS equipment maintenance operation. Based on the above analysis, a basic configuration of a GIS equipment maintenance robot with smart structure, convenient control, and stable motion characteristic has been proposed in this paper. Through the optimization design of the chassis adsorption system, the GIS inspection robot can achieve hanging and vertical adsorption inside the GIS equipment and some special motion status. In this way, the entire GIS equipment can be inspected without blind spots. Through establishing the mechanical model of the GIS inspection robot under the hanging and vertical motion state, the robustness and stability of the GIS inspection robot in the special status have been analyzed, and the corresponding GIS inspection robot robust stability motion control method has also been proposed. Finally, through the integrated design of mechanical system and hardware and software control system, the GIS inspection robot physical prototype system has been developed and the maintenance operation experiment has been carried out in Hunan Electric Power Company Maintenance Company through simulating the internal GIS equipment. The physical prototype can realize the basic movement and special movement in internal GIS equipment without any blind spots and complete the inspection task of GIS equipment operation; the movement of GIS robot is dexterous and stable in the whole operation process, which has strong robust stability. The research of GIS inspection robot in the complex microgrid networks has important theoretical significance and practical application value for the intelligent operation and maintenance management of substation system in complex microgrid networks.

An Edge Computing Method for Partial Discharge under the Power Internet of Things

The recognition of impulse interference for the partial discharge data on the edge computing platform can effectively reduce the false alarm rate in the risk assessment of gas insulated switchgear (GIS). Therefore, an edge computing method for partial discharge is proposed in the paper, which uses the similarity computing among the aggregation of data for each sensor after clustering to match homologous signals. Finally, the algorithm will jointly locate the interference signal and recognize the impulse interference signal by comparing the received signal strength indicator (RSSI) of multiple UHF sensors in/out of GIS equipment. The training set, being consisted of partial discharge and interference signal from the test, is used to contrast the algorithms on the edge computing platform, the result of which indicates that the accuracy rate of the recognition of the impulse interference with the proposed algorithm is 81.4% on the computing platform, with the average processing time for each sample of 7.15s.