On-line Insulation Research Articles

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

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

New Experimental System and Procedure for On-Line Insulation Life Testing of Stator Winding of Automotive Traction PMSM Without Using Dynamo System

New Experimental System and Procedure for On-Line Insulation Life Testing of Stator Winding of Automotive Traction PMSM Without Using Dynamo System

Double-ring high-frequency common-mode switching oscillation current sensor for inverter-fed machine winding insulation monitoring

Double-ring high-frequency common-mode switching oscillation current sensor for inverter-fed machine winding insulation monitoring

Design and Experiment of CSMC Magnet Online Insulation Monitoring System

Design and Experiment of CSMC Magnet Online Insulation Monitoring System

Photon counting technique as a potential tool in micro‐defect detection of epoxy insulation pull rod in gas‐insulated switchgears

AbstractThe micro‐defects in epoxy‐based insulation materials generate a local high electric field which results in continuous degradation, seriously endangering the insulation system of gas‐insulated switchgears. A highly sensitive detection technique is reported for micro‐defects of insulation pull rods based on the photon counting (PC) technique. The results demonstrated that for an epoxy‐based insulation pull rod, the photons released during electroluminescence and ionisation at 2 kV, which is less than the partial discharge inception voltage, can be clearly detected. The findings presented a strong correlation between photon counts and defect severity. Discourse has been conducted to elucidate the mechanism behind defect‐induced PC, employing the amplification of ionising luminescence through electric field distortion induced by micro defects and the augmentation of electroluminescence through the aggregation of trap charge. In this regard, the authors verified that PC can serve as a potential tool in the detection of micro‐insulation defects, which also has huge potential in online insulation condition monitoring.

A novel distributing–collecting on-line insulation monitoring system and line selection technology for the DC supply system

At present, the power supply system of 5G base stations is a micro smart grid, it generally uses 240 V DC power supply with multiple branches, and leakage accidents will threaten personal and property safety, so it is vital to identify the fault line accurately and remove the faults rapidly. In this paper, the leakage phenomenon of transmission lines in the HVDC power supply system of a 5G communication base station is studied. To address the issue of multi-branch line leakage diagnosis and line selection in the 240 V DC system, a new distributed DC insulation monitoring and fault line selection technology and system are proposed. The distributed DC insulation monitoring line selection technology is used to collect the leakage current of each branch by setting a unique single-core DC leakage transformer and process the signal primarily. In addition, a high-resistance bridge switchgear is added to the bus bar. According to different leakage currents caused by the imbalance of the bridge before and after the switch, the insulation resistance of electrodes to the ground is calculated accurately, which solves the problem that the current technology cannot judge the fault of the positive and negative electrodes to the ground at the same time. Through both simulation and prototype experiments, the feasibility of this technology and system device in line insulation monitoring and line selection of the HVDC communication base station power supply system is verified.

Research on an Insulator Defect Detection Method Based on Improved YOLOv5

Insulators are widely used in various aspects of the power system and play a crucial role in ensuring the safety and stability of power transmission. Insulator detection is an important measure to guarantee the safety and stability of the transmission system, and accurate localization of insulators is a prerequisite for detection. In this paper, we propose an improved method based on the YOLOv5s model to address the issues of slow localization speed and low accuracy in insulator detection in power systems. In our approach, we first re-cluster the insulator image samples using the k-means algorithm to obtain different sizes of anchor box parameters. Then, we add the non-local attention module (NAM) to the feature extraction module of the YOLOv5s algorithm. The NAM improves the attention mechanism using the weights’ contribution factors and scaling factors. Finally, we recursively replace the ordinary convolution module in the neck network of the YOLOv5 model with the gated normalized convolution (gnConv). Through these improvements, the feature extraction capability of the network is enhanced, and the detection performance of YOLOv5s is improved, resulting in increased accuracy and speed in insulator defect localization. In this paper, we conducted training and evaluation on a publicly available dataset of insulator defects. Experimental results show that the proposed improved YOLOv5s model achieves a 1% improvement in localization accuracy compared to YOLOv5. The proposed method balances accuracy and speed, meeting the requirements of online insulator localization in power system inspection.

Accelerated Aging Procedure and Online Method for Stator Insulation Monitoring of Induction Motors

Online methods for stator insulation monitoring have been investigated with increasing interest in the last 15 years. These methods rely on the high-frequency behavior of the electric machine. In converter-fed machines, the high-frequency behavior is visible through the current ringings occurring during the switching commutations of the power electronics. As the lifetime of electric machines can last several decades, a faster way to evaluate insulation monitoring methods is through accelerated aging tests. This work aims to perform an accelerated aging procedure on an induction motor to test an online insulation monitoring method. The motor has a randomly-wound stator winding and it is aged by means of thermal cycles without the need for an oven. A custom converter is employed during the accelerated aging procedure to apply the thermal cycles and to record the current ringings. The proposed insulation monitoring method is based on a metric that quantifies the current ringing variations in a simple manner, following the quasi-linear variation of the parasitic capacitances during the motor aging.

Research on early warning method for pollution flashover based on phase angle of insulator leakage current

Pollution flashover is a systematic disaster of power system. It is significant to early warning insulator pollution flashover in real time and accurately. The phase angle of the insulator leakage current is a critical parameter for insulator pollution flashover early warning. However, in field applications, the operating voltage waveform cannot be obtained so that this characteristic parameter cannot be applied to the insulator pollution flashover early warning system. This paper proposes a method to obtain the phase angle of the leakage current of the online insulator. This method avoids the direct measurement of the operating voltage waveform, but uses the stray capacitance between the signal plate of the early warning device metal shell and the transmission line to obtain the operating voltage signal indirectly. In this paper, the method and the early warning mechanism of pollution flashover are analyzed theoretically, and then the early warning device for pollution flashover is designed. Finally, the effectiveness of the early warning device is verified by experiments.

Application of On-line Digital Radiographic Inspection for Pipeline with Insulation

Pipes with insulation are widely used in the industrial field. The traditional nondestructive testing technology for on-line pipes with insulation often requires the removal of a large number of insulation layers and the forcing of the owner to stop for testing. The traditional testing mode not only has low detection efficiency, but also forces the owner to spend a lot of manpower, material resources and financial resources. In recent years, a series of new nondestructive testing (NDT) technologies have different advantages for in-service pipeline testing with coated layers. However, Digital radiographic (DR) testing can satisfy the integrity inspection of on-line pipeline with insulation well. In this paper, the basic principle of DR testing and the configuration of instrument parameters are introduced. Based on the analysis and discussion of the actual testing cases, it is proved that digital ray testing can effectively solve the quality testing of on-line insulation pipeline, and it is a new nondestructive testing technology worthy of further study and popularization.

Detection of Glass Insulators Using Deep Neural Networks Based on Optical Imaging

As the pre-part of tasks such as fault detection and line inspection, insulator detection is a crucial task. However, considering the complex environment of high-voltage transmission lines, the traditional insulator detection accuracy is unsatisfactory, and manual inspection is dangerous and inefficient. To improve this situation, this paper proposes an insulator detection model Siamese ID-YOLO based on a deep neural network. The model achieves the best balance between speed and accuracy compared with traditional detection methods. In order to achieve the purpose of image enhancement, this paper adopts the canny-based edge detection operator to highlight the edges of insulators to obtain more semantic information. In this paper, based on the Darknet53 network and Siamese network, the insulator original image and the edge image are jointly input into the model. Siamese IN-YOLO model achieves more fine-grained extraction of insulators through weight sharing between Siamese networks, thereby improving the detection accuracy of insulators. This paper uses statistical clustering analysis on the area and aspect ratio of the insulator data set, then pre-set and adjusts the hyperparameters of the model anchor box to make it more suitable for the insulator detection task. In addition, this paper makes an insulator dataset named InsuDaSet based on UAV(Unmanned Aerial Vehicle) shoot insulator images for model training. The experiments show that the insulator detection can reach 92.72% detection accuracy and 84FPS detection speed, which can fully meet the online insulator detection requirements.

A Comprehensive Operation Status Evaluation Method for Mining XLPE Cables.

At present, the online insulation monitoring and fault diagnosis of mining cables are extensively discussed, while their operation status assessment has not been deeply studied. Considering that mining cables are closely related to the safe and stable operation of coal mine power supply systems, a comprehensive evaluation method including the Analytic Hierarchy Process (AHP), the membership cloud theory, and the D-S evidence theory is proposed in this paper in order to accurately assess the operation status of the mining XLPE cable. Firstly, the membership cloud is introduced to solve the index membership degree and the weights are calculated by an improved weight vector calculation method. Secondly, the conversion from the base layer indicator membership degree to the target layer trust degree is realized based on the D-S evidence theory. Then, the cable operation status is judged via the trust degree maximum and the distribution of conflict coefficients is further analyzed to warn the indicators with a bad status in the base layer. Finally, the feasibility of the proposed evaluation method is verified by a sufficient and detailed case analysis.

A high‐performance insulators location scheme based on YOLOv4 deep learning network with GDIoU loss function

This paper proposes a Gaussian Distance Intersection over Union (GDIoU) loss function-based YOLOv4 deep learning network to solve the problem of slow speed and low accuracy insulator location in power facilities health inspection. In the scheme, A GDIoU loss function is designed to accelerate the convergence speed of the YOLOv4 deep learning network; at the same time, the GDIoU loss is added as one part of the network propagation loss, and the insulator's location accuracy is accordingly improved. Moreover, a re-location scheme for tilt insulators correction is proposed to enhance the location accuracy of the insulators in different spatial angle states. Large amounts of field insulator images were gathered as training and testing samples to evaluate the performance of the proposed scheme. The experimental results have demonstrated that the GDIoU-based YOLOv4 deep learning network combined with the tilt correction scheme can improve the insulator location speed by three times compared with the peer schemes, and the average precision is increased by 7.37% compared with the naive YOLOv4 network. The performance of the proposed scheme meets the requirement of online insulator location adequately.

Development of an On-line Insulation Degradation Diagnosis Technique for Wind Turbine Generators by Measurement and Analysis of Capacitive Leakage Current for Load Current Variation

Development of an On-line Insulation Degradation Diagnosis Technique for Wind Turbine Generators by Measurement and Analysis of Capacitive Leakage Current for Load Current Variation

Insulation Monitoring of Dynamic Wireless Charging Network Based on BP Neural Network

The transmission cable and power conversion device need to be buried underground for dynamic wireless charging of an expressway, so cable insulation deterioration caused by aging and corrosion may occur. This paper presents an on-line insulation monitoring method based on BP neural network for dynamic wireless charging network. The sampling signal expression of the injection signal is derived, and the feasibility of this method is verified by experiments, which effectively overcomes the problem of large calculation error of insulation resistance when the cable capacitance to ground is large. The experimental results indicate that the error of the proposed method is less than 9%, which can meet the needs of insulation monitoring.

Research on Online Insulation Testing of Power Battery of New Energy Vehicles

The insulation performance of new energy vehicles is an important factor in the normal operation of vehicles. This paper designs a voltage injection-type insulation detection based on the traditional detection.Based on the python language combined with the library provided by NI-visa, it can achieve high integration and meet the national GB/T 18384.1-2015 standard. Experimental results show that the insulation detection system can accurately test the insulation performance of new energy vehicles and meet the new energy vehicle offline detection standards.

A Non-Contact Micro-Ampere DC Current Digital Sensor Based on the Open-Loop Structure

The non-contact accurate measurement of weak direct current (dc) is very important for various fields, such as the online insulation monitoring of power cables. Compared with other dc current sensors, the magnetic modulator is more suitable for dc measurement with its high sensitivity, low noise, and stable working. Most magnetic modulators adopt the closed-loop structure to achieve accurate measurement of large current. And the magnetic modulator only realized the accurate measurement of mA current in the filed of weak dc current, which is difficult to meet the $\mu \text{A}$ current measurement requirements of the online insulation monitoring. Therefore, this article proposes a new structure of open-loop digital magnetic modulator for precision measurement of $\mu \text{A}$ dc current. It adopts an annular shielding layer to weaken the zero offset and stray magnetic field. And a low-frequency excitation source, which can be adjusted with the ambient temperature, is used to improve the temperature stability. Instead of the traditional analog demodulation method, a digital demodulation algorithm is designed to reduce the measurement error caused by the zero offset and excitation source fluctuation. Measurement results show that the new sensor with the absolute error of 0.5 $\mu \text{A}$ can measure 5-1000 $\mu \text{A}$ dc current with a relative error of 0.05% in the full scale. And the sensor can effectively suppress the measurement error caused by the fluctuation of the excitation source and the ambient temperature to less than 0.5%.

A Novel Leakage-Current-Based Online Insulation Monitoring Strategy for Converter Transformers Using Common-Mode and Differential-Mode Harmonics in VSC System

Online insulation monitoring techniques for power transformers have been widely studied. However, challenges rise when they are applied to the converter-connected transformers which are operated under high electrical stress conditions. This article proposes a nonintrusive leakage-current-based insulation monitoring strategy for converter transformers, using inherent characteristic harmonics in voltage source converter system. Analytic relation between common-mode and differential-mode harmonics and insulation capacitances is first presented. Then, a combined strategy is proposed based on the differential measurement of leakage current, which enables online prognosis of not only overall ageing degree but also ageing localization of the phase-to-ground insulation. Experiments are conducted on a 15-kVA transformer driven by an industrial inverter. The proposed combined strategy shows robust performance over effective characteristic frequency spectrums. Several considerations for field implementation are further discussed. The major features of the proposed strategy are as follows: 1) easy implementation and nonintrusive measurement; 2) little impact on system operation and immunity to system interferences; and 3) lower requirements of data processing.

Analytical Analysis and Design of an Advanced Differential-Mode Current Sensor for Insulation Monitoring for Industrial Electrical Assets

Some industrial incidents are attributed to insulation degradation of electrical assets. The leakage current can be a crucial indicator for online insulation monitoring of asset. To realize per-phase monitoring, the measurement accuracy of milliampere-level leakage current is limited by the strong load current noises. This article proposes an advanced current sensor with a dual-core topology based on differential-mode (DM) measurement. An analytical model of magnetic field of the dual-core sensor is originally presented. It helps clarify the significances of the inner core in filtering noises and reducing the influence of system operation and cable positioning. Then, a comprehensive design procedure is fully investigated by selecting the optimal position and approach for magnetic field detection, and a high-speed and low-noise signal conditioning circuit is presented. The proposed current sensor is tested in the laboratory. Experimental results indicate that measurement errors can remain within 0.1mA in the testing scope, where the load current is up to 100A and the DM current varies from 0-7mA. The proposed sensor is further validated with superior performance compared with two commercial industrial sensors. The contribution of this article lies in providing the analytical analysis and design of a novel DM current sensor for leakage current measurement, which achieves online per-phase insulation monitoring for industrial electrical assets.

Acquisition Algorithm of Spatial Control Parameter Tilt Angle for On-line Insulator

Most vision control of the existing power-line insulator robot system needs to be realized indirectly through the remote computer. The vision system itself does not have the ability of vision control, and the level of automation is low. In order to improve the visual control ability and automation level of the power-line insulator robot, an on-line spatial control parameter acquisition algorithm for insulator is presented based on target extraction. The algorithm can be embedded in the hardware platform of visual system to automatically measure tilt angle of insulators. The results of hardware implementation show that the proposed algorithm can measure tilt angle parameters relatively accurately on the hardware platform of visual system. The error of tilt angle measurement is 0.4°~4°, which achieves the desired effect and design requirements.