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The zero-value piece of insulator string can be detected by measuring its electric field strength. The traditional way is always by measuring the electric field distribution curve of the whole string to judge out the zero-value piece position, which is inefficient and difficult to operate in field. In this paper, an improved method of identifying zero-value insulator by just measuring the partial electric field of insulator unit is proposed. Firstly, the simulation model of insulator string zero-value unit detection is established, and the partial electric field distortion characteristic caused by zero-value piece is analysed. Then the partial electric field variation criterion for zero-value identifying is obtained basing on the simulation result. Finally, the identification method is verified by designing the detection device and carrying out the point to point zero-value piece evaluating experiments. The results show that the point to point zero-value insulator detection can be realized using the three-array-probe electric field measuring method. The length of the array should be larger than the length of the electric field distortion interval, which can be taken as 130 mm for the research object in this paper. The identification criteria is that the deviation between probe 1 and 3 is less than 10%, and the deviation between probe 2 and the average is less than 50% at the same time. When detecting zero-value piece at the high-voltage and medium-voltage end, the best position for the sensor device identification is directly facing the steel foot, while for the low-voltage end, the best position is facing the shed edge. The results of the study can provide theoretical support for the intelligent operation and maintenance of external insulation in power transmission and distribution.

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Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the process of power system inspection, manual inspection is difficult and poses safety hazards. The unmanned aerial vehicle platform equipped with intelligent algorithms has a promising prospect of replacing manual insulator detection methods. In response to the shortcomings of slow speed and low accuracy in the detection process of insulator defect targets, an improved YOLOv5 insulator defect fault detection method integrating attention mechanism was proposed. This method integrates the SE attention module and CBAM attention module in the YOLOv5s network, and combines the SE attention module with the C3 module in the network structure to enhance the network’s feature extraction ability. The construction of the self-built insulator dataset was completed through relevant image processing methods. The K-means + + clustering algorithm was used to construct the prior frame of the self-built dataset, and the Mosaic9 data augmentation strategy was introduced, effectively solving the problem of insufficient training data and difficulty in ensuring training effectiveness. Experimental verification shows that the improved detection method improves the accuracy of insulator detection by 9. 7% without affecting the detection time, which has certain reference significance for power system inspection methods. [ABSTRACT FROM AUTHOR]

This article summarizes the research progress of the application of silicone materials in composite insulators, anti-pollution flashover coatings, cable accessories, anti-corrosion coatings, electric insulating oil and other materials, and looks forward to the development prospects of silicone materials in the field of power. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle （UAV） inspections now have emerged as a predominant approach for the examination of transmission lines， with a pivotal focus on the detection of insulator defects. In this context， a lightweight insulator defect detection algorithm， founded on the improved YOLOv5， is introduced. Firstly， the lightweight Ghost convolution is employed to replace conventional convolution. Subsequently， the original feature extraction network is replaced with a repeated weighted bidirectional feature pyramid network （BiFPN） to bolster feature extraction capability across various scales. Finally， the coordinate attention （CA） mechanism is introduced to enhance the efficiency of backbone feature extraction. Experimental results reveal a significant 1.7% enhancement in the average precision of insulator detection， accompanied by a 13.1% reduction in the model’s size. This refined algorithm model not only elevates detection accuracy but also streamlines its footprint， thereby enabling more efficient and rapid insulator defect detection.

Copyright of Electroplating & Finishing is the property of Electroplating & Finishing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to achieve the real-time detection of contamination components on the surface of insulators and explore the impact of different insulators on the identification of contamination components, a hyperspectral remote sensing imaging technology based on feature band score evaluation has been proposed. The data of single fouling components for silicone rubber and ceramics has been collected by hyperspectral imaging technology, and the spectral lines are pre-processed using a model of spectral spatial adaptive total variation and a model of fouling component classification and identification based on the maximum correlation of spectral line features, which was established by a maximum correlation-minimum redundancy-back propagation neural network. The spectral spatially adaptive total variance model was able to denoise and smooth the hyperspectral curves, and obtain hyperspectral images that built a classification and recognition model. The model of maximum correlation-minimum redundancy-back propagation neural network used the fractional ranking of the spectral feature bands as the basis for filtering features, and the classification model is high accuracy, up to 92. 5% for contamination components with silicone rubber substrates and 80% for fouling components using ceramic substrates. The classification of contamination components in the hyperspectrum varies between different types of insulators as background substrates, mainly due to the degree of coverage of the fouling layer and the shape of the fouling particles; it is necessary to avoid thin areas of the fouling layer to reduce the background substrate on the detection results. [ABSTRACT FROM AUTHOR]

In order to improve the detection efficiency of zero value insulator of transmission line, the unmanned aerial vehicles (UAV) equipped with an electric field detection device can realize live detection without climbing the tower. Firstly, the 1:1 simulation model of 220 kV porcelain insulator string is established. Through Comsol finite element numerical simulation, the influence of string type, path jitter, UAV and electric field probe on the spatial electric field distribution of insulator string is analyzed. Then, the identification model and background detection system of zero value insulator are established. Finally, the laboratory and field measurements are carried out. The results show that when double insulator strings have zero value insulators at the same time, their spatial electric field distribution does not affect each other. The offset distance of straight line detection path should be less than 50 mm during UAV flight. The space electric field distribution of insulator string is not affected by UAV and electric field probe. Using the zero-value insulator detection algorithm proposed in this paper, the field detection of zero-value insulators is realized by the UAV equipped with an electric field detection device.

Composition and structure are the two most essential factors for insulator materials performance. In this paper, the differences in composition, structure and mechanical properties of three typical alumina insulator for ultra high voltage direct current (UHVDC) were systematically studied, and the reasons were analyzed. The results show that compared with NGK, two domestic manufacturers have added more calcined industrial alumina, less potash feldspar and quartz into the formula, and have not properly raised the sintering temperature according to the formula, the porcelains of two domestic manufacturers show the following characteristic: 1) the content of Al2O3 are 47.22% and 45.24%, respectively, 7.31% and 5.33% higher than that of NGK, the content of SiO2 are 46.32% and 48.44%, respectively, 7.09% and 4.97% lower than that of NGK, and the total content of K2O + Na2O are 3.21% and 3.14%, respectively, 0.41% and 0.48% lower than that of NGK; 2) the true porosity are 5.24% and 4.18%, respectively, 3.46% and 4.52% lower than that of NGK. The internal pores are basically less than 5 µm, and the pore size is generally smaller. There are many flat, slender, crack like pores, as well as annular pores and cracks surrounding the particles; 3) the content of internal crystalline phase is 48.10% and 49.04%, respectively, which is nearly 10% higher than NGK, with corundum phase being 10% higher, quartz phase being 4% higher, mullite phase being 4% less. The mullite phase is thinner and shorter, and the degree of crosslinking is lower; 4) the average value of the three-point bending strength of the porcelain head test strip are 155 and 158 MPa, respectively, which are higher than NGK, but the dispersion strength of the two domestic manufacturers is higher than NGK. [ABSTRACT FROM AUTHOR]

BackgroundSelf-powered neutron detectors (SPNDs) are critical devices in the monitoring and protection systems of nuclear reactors, and their signal current directly reflects the value and distribution of the core power. Insulators play an essential role in the design of SPNDs and are the main factor affecting the calculation accuracy of the signal current.PurposeThis study aims to improve the accuracy of the calculation method of the SPND signal current, ensuring that the measured currents accurately reflect the reactor conditions and meet the highest industrial standards.MethodsFirstly, the signal generation mechanism of the SPND was thoroughly discussed, and three independent calculation methods of the current based on the inherent physical characteristics of the space electric field of an insulator were proposed. Then, high-fidelity simulations of the SPND were performed using the Monte Carlo code, and the three methods were validated based on the simulation results. In addition to the current caused by the neutrons, the current caused by the photons inside the reactor was quantitatively analyzed. Meanwhile, extensive radiation experiments on the various reactors have been performed to verify these three current calculation methods.ResultsThe difference between the results obtained by using the three methods is less than 1%, demonstrating a considerable accuracy. In addition, the current of the rhodium SPND is primarily owing to the neutrons, whereas the photon-induced current is generally less than 5%. Experimental verification results on the several operating reactors show that the difference between the theoretical and experimental results is less than 3%, which also proves its effectiveness and accuracy.ConclusionsThis method has been applied to the large Chinese Gen-III advanced pressurized water reactor (HPR1000) and is universal. It can be used for the signal analysis of different types of SPNDs, as well as for providing valuable references for core monitoring systems in other reactors, such as the Gen-IV fast reactor as well as future fusion reactors.

The uncertainty of haze weather makes it difficult to carry out research on its impact on power equipment. However,whether the research under simulated haze environment is equivalent to that under actual haze weather is related to the guiding value of the research conclusion to the actual work. For this reason,in this paper,the samples are contaminated under simulated haze environment and natural haze environment respectively. By testing the surface contamination layer resistance and leakage current of the contaminated samples under the two environments,the equivalence of the effects of the two environments on insulators is analyzed and discussed. It is found that from the measured data of salt density,ash density and surface pollution layer resistance,the insulators under the two haze environments have an equivalent corresponding relationship,that is,the insulators with pollution accumulation under natural haze for one year are equivalent to those with pollution accumulation under simulated haze for 10-20 days. However,from the perspective of leakage current parameters,composite insulators under the two haze environments are not equivalent,so the conclusion of leakage current under the simulated haze cannot be directly used to guide the actual work.

In the application process of insulator defect detection model, it is necessary to detect a variety of target domain images. However, due to the different distribution of images in different target domains, it is difficult for the defect detection model trained in the source domain to achieve good detection results in different target domains. Aiming at the problem of low generalization of defect detection models, a transmission line defect detection method based on domain knowledge transfer was proposed. First, a new feature fusion method was adopted to improve the feature extraction ability of defective targets. Then image-and instance-level-based domain classifiers were implemented through adversarial training, and domain transfer was realized at image and target levels. A region-level domain classifier was introduced into the network, which further realized the alignment of the source and target domains at the region level. The experimental results show that the model can alleviate the problem of poor detection effect caused by the imbalance of image distribution in different domains, and achieve better defect detection effect compared with other target detection methods. [ABSTRACT FROM AUTHOR]

In recent years, the external insulation discharge and pollution flashover of transmission lines have occurred several times in the vicinity of nickel plant in Yancheng, and they cannot be effectively suppressed after the use of composite insulators. This phenomenon does not happened in the nearby chemical zone. Therefore, pollution samples of insulators are conducted in nickel plants and chemical plants within the jurisdiction of Yancheng power grid for analyzing and testing pollution chemical components, including anion and cation content, insoluble element composition, main compound composition. Then, based on the results of component analysis, a test under mixed pollution is carried out to obtain the flashover characteristics of composite insulators under typical pollution components of different pollution sources. Comparison between pollution composition of near chemical plants and nickel plants indicates that the latter has higher nitrates densities but lower chloride salts density. For one large and two small composite insulators, the flashover gradient under the typical pollution of nickel plants is 5% to 10% lower than that of chemical plants. For the external insulation configuration in heavy industrial dust areas, it is recommended to use one large and one small insulator to improve the electrical strength.

Aiming at the problem that it is difficult to detect insulators and their defects in real time and efficiently in the course of electric patrol inspection of UAV,an improved insulator defect detection model based on CenterNet is proposed.Firstly,lightweight network EfficientNet-B0 is used to replace the original model’s feature extraction network ResNet18,which ensures the model extraction ability and speeds up its reasoning speed.Then,a feature enhancement module FEM is built,which distributes the weight of the feature channels after upsampling reasonably and suppresses invalid features.Using FPN (feature pyramid networks) for reference,the shallow and deep features are integrated to enrich the information of feature layer.Secondly,the coordination attention(CA) mechanism,which is a mixture of space and channel,is introduced into CenterNet-Head,which makes the prediction of category and location information more accurate.Finally,Soft-NMSis used to solve the problem of “single target and multiple frames” caused by inaccurate prediction of center points in the process of model detection.Experimental results show that the precision of the improved CenterNet is improved by 11.92%,the speed is increased by 8.95 FPS,and the model size is reduced by 54 MB.Compared with other detection models,the accuracy and speed are improved,which proves the real-time performance and robustness of the improved model.

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Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rubber Industry is the property of Editorial Office of China Rubber Industry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In a large environment where the power system needs to be inspected, the traditional method of manual inspection has great inconvenience and potential safety risks, and the object detection method of unmanned aerial vehicle has great application prospects in the direction of insulator detection and recognition.SE-YOLOv5s, a lightweight insulator detection network that performs efficient detection for this task was presented.Firstly, backbone of YOLOv5s by fusing the SE attention module was strengthen.Then, the position distribution of insulator object was investigated and predefined templates of a priori box by K-means clustering on prior coordinate vectors were generated.Finally, the network by a multitask loss function was trained combined with confidence and position regression task.Furthermore, Mosaic data augmentation was utilized to supplement additional training samples.Experimental results demonstrate that the proposed SE-YOLOv5s significantly outperforms baseline methods at multiple criterions including accuracy, recall rate, detection rate and mean average precision.In comparison with the baselines, the proposed network has a flexible trade-off between robustness and memory overhead and it is a potential approach to promote the power system development.

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To construct a retroviral vector-mediated SynNotch gating system. Methods The retroviral vector was designed and modified，insulator sequences were inserted at both ends，and the SynNotch gating system was constructed based on the Gal4/UAS gene expression regulation system. First，a retroviral vector was used to construct a gated plasmid containing only Gal4-VP64 fusion protein transcriptional regulatory elements and an effector plasmid contain⁃ ing the UAS activation sequence. The effector plasmids used green fluorescent protein（EGFP）and luciferase as reporter genes，and were divided into 6 types，three of which were designed for the forward sequence，and the UAS sequence and the reporter gene were inserted into the retroviral vector（5'-3'）in the forward direction，including the forward non-insula⁃ tor，forward single insulator，and forward double insulator，respectively；the other three were designed for reverse se⁃ quence，and the UAS sequence and the reporter gene were reversely inserted into the retroviral vector（3'-5'），including reverse non-insulator，reverse single insulator，and reverse double insulator，respectively. After the plasmid was construct⁃ ed，it was verified by sequencing，and the successfully sequenced plasmid was further prepared by virus packaging to pre⁃ pare viral vectors，including gated vectors and effector vectors. PCR method was used to detect retrovirus titers. The viral vector was transduced into 293T cells，the 293T cells transduced by the effector vector alone were used as the uninduced group，and the 293T cells co-transduced by the gated vector and the effector vector were used as the induced group. Flow cytometry and luciferase chemiluminescence experiment were used to detect the expression efficiency of reporter genes in the two groups. Results The titers of retroviral vectors were 107 -108 copies/mL，and were not lower than those of the pos⁃ itive control. The results of flow cytometry showed that the expression efficiencies of EGFP were 95. 5%，69. 7%，and 76. 2% in the three forwardly designed effector vectors without induction，and the expression efficiencies of EGFP after in⁃ duction were 99. 9% ，95. 3%，and 97. 1%，respectively. The three reversely designed effector vectors had EGFP expres⁃ sion efficiencies of 0. 085%，0. 61%，and 7. 4% without induction，and EGFP expression efficiencies of 59. 9%，77. 9%， and 89. 5% after induction. The detection results of the luciferase chemiluminescence experiment showed that the fluores⁃ cence intensity of luciferase expression of the three effect vectors in the forward design was above 104 without induction， and the fluorescence intensity after induction was above 105 . The fluorescence intensity of luciferase expressed by the effec⁃ tor vector was below 3×103 without induction，and the fluorescence intensity after induction was above 104 . The fluores⁃ cence intensity after induction was significantly different from that before induction（P<0. 01）. Among them，the fluores⁃ cence intensity after induction by the reverse double insulator design reached more than 105 . Conclusion A retroviral vector-mediated SynNotch gating system was successfully constructed in this study. [ABSTRACT FROM AUTHOR]

In deep learning training, insulator detection requires a large number of faulty insulators. It is actually difficult to obtain a large amount of faulty insulator data. Generative Adversarial Network (GAN) provides a feasible solution for augmenting training samples. This paper supplements the defective insulator samples in the structure of the Cycle-consistent GAN (CycleGAN), optimizes the model by changing the loss function, and inputs the image synthesized by the forward generator to the reverse generator, thus maintaining the overall outline of the sample while adding the difference. In the SSD (Single Shot Detector) target detection experiment, the method of using the improved CycleGAN model to expand the dataset was compared with other GAN models. The results show that the method of using the improved CycleGAN to expand the dataset significantly improves the recognition rate of insulator drop detection compared with other expansion methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, it is difficult for the electric power transmission and distribution network to detect the insulators insulation deterioration in operation. In view of this problem, on the basis of the analysis of the insulation deterioration mechanism, this study proposes an intelligent method based on the feature - fused CNN to detect whether the insulator produces the insulation deterioration. Firstly, the flashover experiment is conducted on the insulator to produce three states as follows: no discharge, weak discharge and strong discharge. Subsequently, the ultraviolet imager is applied to collect the CNN insulators ultraviolet images in different discharge state lo form the sample library. Then, the CNN is adopted to extract the samples features and fuse the extracted shallow and deep features. Finally, the fusion feature map is identified and classified to judge whether the insulator is degraded. The result reveals that this method has a high detection rate, and the highest correct rate reachs 97.4%. Compared to the AlexNet algorithm, this method has a higher accuracy and quicker convergence speed. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Fuzhou University is the property of Journal of Fuzhou University, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of South China Normal University (Natural Science Edition) / Huanan Shifan Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of South China Normal University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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To make sure that the transmission circuits work safely and stably, it is necessary to inspect the circuits on a regular basis. As the core component of transmission line?the insulator detection plays an important role in the inspection of transmission line fault in power grid. For unmanned aerial vehicles with a large number of complex background and low resolution images of aerial insulators, the traditional manual feature extraction method and classical convolutional neural network identification method are easy to lose location and details, and the location accuracy is relatively low. It is difficult to accurately identify the insulator images in complex background. Therefore, an aerial photo insulator detection method based on deep learning U-net network is proposed, automatic extraction of hierarchical feature, through the overlapping method combining the characteristics of shallow and high dimensional features, the high resolution feature map of the shallow layer is used for pixel location, and the deep high dimensional feature graph is used to classify the pixels. It avoids the loss of detailed information such as the location of the target, improves the positioning accuracy, and effectively detects the insulator under complicated background. The accuracy rate reaches 88.9% .which is an effective insulator detection method. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to apply the optical electric field sensor technology to the degradation research of insulator, a new type of optical sensing detection system based on Pockels effect is designed to solve the problem that the existing low-quality insulators need contact detection with power failure and are affected by temperature and humidity to a large extent. The new system enables the advantages including online detection without contact, handy operation, high accuracy and high efficiency. By simulation and experimental analysis, The feasibility of the designed system is verified. The results show that the optical field sensing detection system can realize the non-contact detection of the insulator degradation under high pressure environment, which is of great significance to solve the non-contact detection problem of the insulator under high pressure. [ABSTRACT FROM AUTHOR]

Flash infrared thermography (IRT) is a non-contact optical non-destructive technique, which has a promising application in the on-line inspection of disbonds in insulator of solid rocket motors (SRMs). In order to promote the application of IRT in the disbond inspection of insulators in SRMs, the basic regularities of pulsed thermography (PT) of disbonds in the insulator of SRM cases are studied by using numerical simulation and experiment. The relationships between decision-making parameters and structure parameters are obtained, the criterion to determine detection limits is proposed, the detection limits of disbond size and depth (i.e. insulator thickness) are calculated, and the method to characterize the disbond size and the error correction formula are provided. The equations, data and conclusions provide a scientific basis and operational guidelines for the infrared thermography of disbonds in insulator of SRMs. [ABSTRACT FROM AUTHOR]

With the popularization of high or extra-high voltage power transmission and the continuous improvement of grade of wire and voltage, some power grids frequently have pollution flashover accidents. Therefore, it is very important to effectively and accurately obtain the insulator contamination grade for the safe operation of power grid. Currently, major detecting methods of insulator contamination grades include equivalent salt deposit density method, leakage current method, frequency spectrum method, and so on. Although above mentioned methods are feasible, basically, all of them mainly focus on the treatment of single insulator without paying attention to the entire group of insulators' heating features and appearance characteristics of the heating zone. Therefore, according to the heating features of polluted insulators, a new detecting method of porcelain insulator contamination grade, which was based on color image treatment, was proposed under the purpose of simplifying the procedures as well as reducing the misdetection ratio of the present detecting method. It meant that the assessment of samples' contamination grades would be performed under a color-image-based treatment on the obtained insulator infrared thermogram. Firstly, the infrared thermograms, which were taken at the scene, were denoised by two-step method (Gaussian noise was denoised by the wavelet adaptive diffusion after the impulse noise was denoised by the median filtering method) to obtain the filtered insulator thermogram. Secondly, they were segmented based on the differences of the component R, G, and B respectively shown by the normal insulators, the broken insulators, and the corresponding infrared thermograms. Although the traditional OTSU segmentation algorithm is simple and effective, it is not suitable to apply on the image segmentation without clear bimodal histogram. Therefore, the presented paper completed the image threshold segmentation by combining the characteristics of wavelet transform and OTSU. The modified OTSU was adaptive to choose the optimal threshold value, and improved the obtained image quality after the segmentation. However, the binary images obtained from the segmentation still had lots of noise points and cavities. Therefore, the mathematical morphology of the obtained binary image was modified twice in order to obtain a polluted area of the insulator. Thirdly, 5 feature components, including ratio of area to perimeter, average value, extreme value, standard deviation as well as major-minor axis ratio of the smallest outer ellipse, were extracted from the R channel of the polluted area by taking advantage of a statistical method, and the back propagation (BP) neural network was trained. During the training, the transfer function of the hidden layers was the logarithmic transfer function, logsig. The neural transfer function of the output layers was the linear activation function, purelin, which regarded image characteristic values as the input of the network, and the salt deposit density as the output that would be transformed to the corresponding contamination grade. Consequently, a detecting model of contamination grades was constructed on the basis of the color image. Finally, the running conditions of the insulators in the Qitaihe Power Supply Company were tested, among which the relative humidity, one of the shooting conditions, was at 78%- 91% in the experiment. However, the temperature of the experiment was adjusted as 10-30 I because the infrared thermogram was hardly affected by the shooting temperature. The contamination grade of each insulator was recorded according to the regulations of GB/T 5582- 4993 when each infrared thermogram was shot. A total of 500 groups of infrared thermograms were obtained from XP- 70 porcelain insulator with 0,I,II, III and IV polluted grade respectively, which were selected as experimental samples. This system was tested repeatedly, and the overall detecting precision reached above 91%. The experimental result shows that the insulator contamination grades are more accurate when the appearance characteristics of polluted insulators and the features of infrared thermogram are combined during the detection. It can also be noted that such a method can be served as the foundation of the detection of insulator contamination grades in the complicated outdoor environment. [ABSTRACT FROM AUTHOR]

In order to study the effects of different chemical materials′s conductivity property on the conductivity of insulator surface pollution, according to the current measurement results of insulator natural contamination components ,such typical soluble chemicals as KNO3 、NaCl、CaSO4 、KCl、NH4 NO3 、NaCl were taken as the object, and their conductive performance under water as well as the differences were studied. Research results indicate that it is not proper to use only equivalent salt deposit density (ESDD) and NaCl to describe the electrical property of polluted insulator. During artificial tests, influence of dissolving time on the conductivity of soluble material can be neglected. When measuring ESDD, it is suggested that the pollution liquid should be stewed and measured after more than 5 min. The conclusions are of certain value in referring the conductions of ESDD measurements, flashover voltage modification and laboratory artificial simulation tests. [ABSTRACT FROM AUTHOR]

In order to achieve real-time detection of contamination wetting state to prevent the pollution flashover caused by the insulator surface contaminating wetting, the wetting state detection system of insulator surface contamination is designed based on phase angle difference, the measuring circuit and various parts of system with the powerful circuit simulation software Multisim 12 are used to conduct the simulation. According to the simulation results, the system can detect the wetting state of the insulator surface surface contamination. [ABSTRACT FROM AUTHOR]

In order to have a real-time monitoring of the heavy contamination on the surfaces of insulators in high voltage overhead wire, a system based on distributed fiber optical is proposed. According to the analogy method, the fiber optical cable is put into the same environment with the insulators, so that the salt density in the optical cable can be regarded as that in insulators. Firstly, the loss of optical power during transmission is measured, and then such measurable values as the loss of optical power, air humidity and surrounding temperature are adopted as inputs of successfully trained neural network to calculate the salt density. Experimental data proves the effectiveness and the measurement accuracy of the designed system. [ABSTRACT FROM AUTHOR]

Aiming at the insulator sheds' dropping off, study on image threshold segmentation, edge detection, insulator recognition and fault diagnosis were carried out, characteristics of insulator and the background area were summarized, a new method to recognize and diagnose fault insulator was proposed. The OTSU was applied for image segmentation, and wavelet modulus maximum method for edge detection, Hough transform was improved to detect ellipsis quickly, ellipse parameters were used to design the classification standards and accurately recognize the insulator. Finally, fault diagnosis was realized based on location information. The method was verified using aerial images. The results indicate that ellipse parameters can be calculated, functions such as edge detection, insulator recognition, and fault diagnosis can be realized, and the algorithm responds quickly and accurately. [ABSTRACT FROM AUTHOR]

The genome architectural protein CTCF (CCCTC-binding factor) not only mediates long-distance chromatin interactions between distal enhancers and target promoters, but also functions as an important insulator-binding factor to block improper enhancer activation of non-target promoters, and is thus of great significance to transcriptional regulation of developmental genes. The Hox (Homeobox) gene family plays an important role in the development of the brain, bones, and limbs. The spatiotemporal colinear expression of the HOXD cluster along the proximal-distal axis of limbs is regulated by two clusters of enhancers known as super-enhancers located in the flanking regulatory regions. We focused on the HOXD cluster to explore the architectural role of CTCF in transcriptional regulation of developmental genes. The HOXD cluster contains 9 paralogous genes intermixed with a series of CBS (CTCF-binding site) elements. Using the CRISPR DNA-fragment editing system, we generated a series of single-cell HEK293T clones with deletion of increasing numbers of reverse CBS elements. RNA-seq experiments revealed decreased levels of HOXD gene expression. In addition, chromosome conformation capture experiments revealed increased long-distance chromatin interactions between HOXD and the upstream enhancer cluster and corresponding decreased interactions between HOXD and the downstream enhancer cluster. Thus, tandem reverse CTCF sites function as insulators to maintain HOXD regulatory balance between the upstream and downstream enhancer clusters. This study has interesting implications on the precise gene expression control of the Hox family during animal development.

CTCF (CCCTC-binding factor) is a zinc-finger protein which plays a vital role in the three-dimensional (3D) genome architecture. A pair of forward-reverse convergent CTCF binding sites (CBS elements) mediates long-distance DNA interactions to form chromatin loops with the assistance of the cohesin complex, while CBS elements at the chromatin domain boundaries show reverse-forward divergent patterns and function as insulators to discriminate against DNA interaction between chromatin domains. However, there are still many unresolved problems regarding CTCF-mediated insulation function. In order to study the connections between chromatin loops and the insulation function of CBS elements, we combinatorically inverted CBS elements at the HOXD locus by using CRISPR/Cas9 DNA-fragment editing methods in the HEK293T cell line and obtained five different kinds of single-cell CRISPR clones. By performing quantitative high-resolution chromosome conformation capture copy (QHR-4C) experiments, we found that boundary CBS inversions abolish original chromatin loops and establish new loops from the opposite direction, thus shifting the insulator boundary to the new divergent CTCF sites. Furthermore, tandem CBS elements block cohesin permeated from the opposite orientation to function as insulators. RNA-seq experiments showed that alterations of local three-dimensional genome architecture would further influence gene expression of the HOXD cluster. In conclusion, a pair of divergent CBS elements function as insulators by forming chromatin loops within chromatin domains to block cohesin sliding.