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Aerial images are commonly used for detecting insulators in transmission lines to ensure their safe operation. However, each capture session generates thousands of insulator images, requiring manual collection, organization, and analysis. Therefore, to achieve automation in insulator state detection, this paper proposes a method based on deep learning for insulator state detection in transmission lines. Firstly, an insulator state detection model is built based on YOLOv7, and the model is improved using a bi-level routing attention mechanism and a content-aware up-sampling operator. Then, combined with dataset augmentation, including cropping, flipping, rotating, scaling, and splicing and a bounding box loss function incorporating a dynamic non-monotonic focus mechanism, 4000 visible images from different voltage levels of transmission lines are used for training. Finally, using a confusion matrix combined with comparative and ablation experiments, the results of insulator state detection are analyzed. Experimental results show that the proposed method achieves a detection accuracy of 97.1%. The detection accuracies for insulators exhibiting self-explosion, damage, flashover, and insulator strings are 93.5%, 98.6%, 97.5%, and 98.9%, respectively. Analysis results demonstrate that the proposed method can effectively realize insulator status detection.

In this paper, a method for detection of crack locations and the width of basin insulators is proposed. Based on the optical frequency domain reflectometry (OFDR) system, the system utilizes an FBG with high feedback for strain as well as temperature, which is affixed to the surface of the tub insulator, and a common single-mode optical fiber, which is used for transmitting data and connected to the optical backscattering reflectometry interrogator. The interrogator measures the backscattered light from the FBG, which varies with temperature or strain. The method has been used to measure the location and width of several different cracks and can locate the crack position with a spatial resolution of 1 mm and measure the crack width with a resolution of 0.77 mm. The method has been used to measure the position and width of insulators. This method provides a simple and fast approach to crack detection in insulators.

Aiming at the problems of a large volume, slow processing speed, and difficult deployment in the edge terminal, this paper proposes a lightweight insulator detection algorithm based on an improved SSD. Firstly, the original feature extraction network VGG-16 is replaced by a lightweight Ghost Module network to initially achieve the lightweight model. A Feature Pyramid structure and Feature Pyramid Network (FPN+PAN) are integrated into the Neck part and a Simplified Spatial Pyramid Pooling Fast (SimSPPF) module is introduced to realize the integration of local features and global features. Secondly, multiple Spatial and Channel Squeeze-and-Excitation (scSE) attention mechanisms are introduced in the Neck part to make the model pay more attention to the channels containing important feature information. The original six detection heads are reduced to four to improve the inference speed of the network. In order to improve the recognition performance of occluded and overlapping targets, DIoU-NMS was used to replace the original non-maximum suppression (NMS). Furthermore, the channel pruning strategy is used to reduce the unimportant weight matrix of the model, and the knowledge distillation strategy is used to fine-adjust the network model after pruning, so as to ensure the detection accuracy. The experimental results show that the parameter number of the proposed model is reduced from 26.15 M to 0.61 M, the computational load is reduced from 118.95 G to 1.49 G, and the mAP is increased from 96.8% to 98%. Compared with other models, the proposed model not only guarantees the detection accuracy of the algorithm, but also greatly reduces the model volume, which provides support for the realization of visible light insulator target detection based on edge intelligence.

Insulators on overhead power lines have long been exposed to the outdoors and are susceptible to pollution and salt contamination. Due to factors such as wind and gravity, pollution in the atmosphere gradually deposits on the surface of the insulator. In humid and windy conditions, conductive pollutants begin to dissolve in the water on the surface of the insulator, increasing the leakage current and affecting insulation performance. This study mainly uses a data acquisition system to measure the leakage current of the insulator and weather parameters (including temperature, relative humidity, pressure, wind speed, and ultraviolet) around the insulator. Artificial intelligence is then applied to establish a prediction model for leakage current based on weather parameters. The established model accurately predicts insulator leakage current through weather parameters. In order to observe the real-time status of the insulator, this study establishes a monitoring platform that integrates the predicted leakage current with weather parameters. It allows users or maintenance personnel to connect to the server through the network to observe the predicted results and weather parameters. The results can establish a real-time salt contamination monitoring system for insulators on transmission lines, enabling operation and maintenance personnel to understand the actual insulation situation of the insulator in real-time. This can not only prevent power outages due to salt contamination or pollution but also reduce the workload for maintenance personnel. Moreover, the maintenance strategy is upgraded from time-base maintenance to condition-base maintenance, significantly improving the efficiency of operation and maintenance for power lines.

Insulators, pivotal to the integrity of railway catenaries, demand impeccable functioning to prevent system failures. Their consistent assessment is vital for railway safety. Current insulator evaluations in subways predominantly involve human intervention, a method fraught with inefficiencies, inaccuracies, and oversights, exacerbated by the complex backdrop of subway tunnels and minuscule defect dimensions. This study introduces an enhanced algorithm, anchored in the YOLOv5 framework, to refine insulator defect identification. Challenges in defect detection include limited, imbalanced data samples and adaptability. Addressing this, an accurate catenary model mirrors the subway line’s architecture, facilitating the creation of synthetic instances of both intact and impaired insulators. An atomization technique augments the dataset volume, fortifying the algorithm’s resilience in reduced visibility conditions, such as fog. Tackling sample equilibrium, the study introduces an equilibrium loss function, assigning disparate weights to various sample categories during training, thereby sharpening the algorithm’s focus on positive instances, particularly those that are challenging to discern, and rectifying the disproportion in sample categories. Incorporating lightweight structures like GhostNet and the Efficient Channel Attention Network (ECA-Net) channel attention scheme not only diminishes the network’s computational demands, thereby elevating the detection capabilities, but also minimizes superfluous data processing, enhancing the accuracy in identifying smaller targets. Empirical analyses indicate substantial model optimization: a size reduction to 60 pp of its original (from 15 MB to 9 MB), a near 1.4 pp increase in mean average precision (mAP) to 96.57%, and a tripling of the detection speed (from 30 to 90 FPS). Real-world image assessments further reveal a mAP improvement of approximately 2.5 pp (reaching 98.43%), confirming the model’s suitability for real-time applications.

Insulators find extensive use across diverse facets of power systems, playing a pivotal role in ensuring the security and stability of electrical transmission. Detecting insulators is a fundamental measure to secure the safety and stability of power transmission, with precise insulator positioning being a prerequisite for successful detection. To overcome challenges such as intricate insulator backgrounds, small defect scales, and notable differences in target scales that reduce detection accuracy, we propose the AC-YOLO insulator multi-defect detection network based on adaptive attention fusion. To elaborate, we introduce an adaptive weight distribution multi-head self-attention module designed to concentrate on intricacies in the features, effectively discerning between insulators and various defects. Additionally, an adaptive memory fusion detection head is incorporated to amalgamate multi-scale target features, augmenting the network’s capability to extract insulator defect characteristics. Furthermore, a CBAM attention mechanism is integrated into the backbone network to enhance the detection performance for smaller target defects. Lastly, improvements to the loss function expedite model convergence. This study involved training and evaluation using publicly available datasets for insulator defects. The experimental results reveal that the AC-YOLO model achieves a notable 5.1% enhancement in detection accuracy compared to the baseline. This approach significantly boosts detection precision, diminishes false positive rates, and fulfills real-time insulator localization requirements in power system inspections.

Cross-linked polyethylene (XLPE) cable is a representative power transmission cable. XLPE has excellent mechanical properties, chemical and heat resistance, and insulation. However, XLPE insulation deteriorates during operation due to electrical, mechanical, and thermal stresses. Among these, thermal stress is a major factor and reduces insulation properties due to a change in molecular structure. Therefore, XLPE characteristic evaluation by heat exposure is essential for power cable condition evaluation. Herein, deteriorated XLPE samples were characterized by tensile strength, X-ray diffraction, Fourier-transform infrared spectroscopy, differential scanning calorimetry, and ultraviolet-visible spectroscopy after exposure to various temperatures and durations. Comparing the tensile strength with other analysis results yielded correlations. Each characteristic showed a linear relationship. The correlation between tensile strength and carbonyl index was the strongest, and the coefficient of determination, R2, was 0.9299. Therefore, these results will provide important information on chemical properties when establishing operational management standards for XLPE insulators in the future.

Railway system insulators are affected by pollution, altitude, and other environmental situations during operation, which causes reduced electrical performance or even flashover accidents. These factors threaten the safety of railway operations in high-altitude areas. However, the natural contamination characteristics of a railroad in a plateau area is still unclear. In this study, a natural pollution accumulation test for railway insulators in a high-altitude area was carried out, and the distribution rules of nonsoluble deposit density (NSDD), equivalent salt deposit density (ESDD), NSDD/ESDD ratio, and nonuniformity (T/B) of the pollution distribution of the tested insulators were calculated. Meanwhile, the chemical compositions of the pollution from different test sites were analyzed. The differences of pollution accumulation between railway insulators and suspended insulators of a power system and the influencing factors were compared and analyzed by combining with a numerical simulation. The results show that the pollution level of railroad insulators is mainly distributed in level b and above. A pollution sample is mainly composed of sodium chloride, while the NSDD/ESDD ratio of pollution is mostly distributed from 0 to 5, with T/B value ranges from 1/0.62 to 1/1.76. The amount of insulator contamination is influenced by the location inside and outside the tunnel. Additionally, the pollution amount is influenced by the structure and type of insulators. Finally, this paper studies the creepage distance and structural height required by railway insulators in a plateau area according to the natural pollution accumulation characteristics of railway insulators, which can provide a reference for a railway electrical external insulation configuration in a high-altitude area.

Acoustic inspection is a valuable technique that can detect early stage defects in equipment, thereby facilitating predictive maintenance. In recent times, ultrasonic sensors have made detecting partial discharges through acoustic sensing increasingly feasible. However, interpreting the acoustic signals can pose challenges, as it requires extensive expertise and knowledge of equipment configuration. To address this issue, a technique based on zero-crossing rate and fundamental frequency estimation has been proposed to standardize insulator diagnosis. In an experiment involving a database of 72 raw acoustic signals with frequencies ranging from 0 to 128 kHz, various types of pollution and defects were introduced to a chain of insulators. By employing the proposed technique, the occurrence of partial discharges can be detected and classified according to type, such as corona or surface discharges. This advanced approach to diagnosis simplifies the process while providing valuable insights into the severity of observed phenomena in the field.

At this stage, the inspection of transmission lines is dominated by UAV inspection. Insulators, as essential equipment for transmission line equipment, are susceptible to various factors during UAV detection, and their detection results often lead to leakages and false detection. Combining deep learning detection algorithms with the UAV transmission line inspection system can effectively solve the current sensing problem. To improve the recognition accuracy of insulator detection, the MS-COCO pre-training strategy that combines the FPN module with a cascading R-CNN algorithm based on the ResNeXt-101 network is proposed. The purpose of this paper is to systematically and comprehensively analyze mainstream isolator detection algorithms at the current stage and to verify the effectiveness of the improved Cascade R-CNN X101 model by combining the mAP (mean Average Precision) value and other related evaluation indices. Compared with Faster R-CNN, Retina Net, and other detection algorithms, the model is highly accurate and can effectively deal with the false detection, leakage, and non-recognition of the environment in online special detection. The research in this paper provides a new idea for intelligent fault detection of transmission line insulators and has some reference value for engineering applications.

To address the issue of low accuracy in insulator object detection within power systems due to a scarcity of image sample data, this paper proposes a method for identifying insulator objects based on improved few-shot object detection through feature reweighting. The approach utilizes a meta-feature transfer model in conjunction with the improved YOLOv5 network to realize insulator recognition under conditions of few-shot. Firstly, the feature extraction module of the model incorporates an improved self-calibrated feature extraction network to extract feature information from multi-scale insulators. Secondly, the reweighting module integrates the SKNet attention mechanism to facilitate precise segmentation of the mask. Finally, the multi-stage non-maximum suppression algorithm is designed in the prediction layer, and the penalty function about confidence is set. The results of multiple prediction boxes are retained to reduce the occurrence of false detection and missing detection. For the poor detection results due to a low diversity of sample space, the transfer learning strategy is applied in the training to transfer the entire trained model to the detection of insulator targets. The experimental results show that the insulator detection mAP reaches 29.6%, 36.0%, and 48.3% at 5-shot, 10-shot, and 30-shot settings, respectively. These findings serve as evidence of improved accuracy levels of the insulator image detection under the condition of few shots. Furthermore, the proposed method enables the recognition of insulators under challenging conditions such as defects, occlusion, and other special circumstances.

For high electron mobility transistors (HEMTs) power transistors based on AlGaN/GaN heterojunction, p-GaN gate has been the gate topology commonly used to deplete the two dimensional electron gas (2-DEG) and achieve a normally-OFF behavior. But fully recessed MIS gate GaN power transistors or MOSc-HEMTs have gained interest as normally-OFF HEMTs thanks to the wider voltage swing and reduced gate leakage current when compared to p-GaN gate HEMTs. However the mandatory AlGaN barrier etching to deplete the 2-DEG combined with the nature of the dielectric/GaN interface generates etching-related defects, traps, and roughness. As a consequence, the threshold voltage (VTH) can be unstable, and the electron mobility is reduced, which presents a challenge for the integration of a fully recessed MIS gate. Recent developments have been studied to solve this challenge. In this paper, we discuss developments in gate recess with low impact etching and atomic layer etching (ALE) alongside surface treatments such as wet cleaning, thermal or plasma treatment, all in the scope of having a surface close to pristine. Finally, different interfacial layers, such as AlN, and alternative dielectrics investigated to optimize the dielectric/GaN interface are presented.

The accuracy of insulators and their defect identification by UAVs (unmanned aerial vehicles) in transmission-line inspection needs to be further improved, and the model size of the detection algorithm is significantly reduced to make it more suitable for edge-end deployment. In this paper, the algorithm uses a lightweight GhostNet module to reconstruct the backbone feature extraction network of the YOLOv4 model and employs depthwise separable convolution in the feature fusion layer. The model is lighter on the premise of ensuring the effect of image information extraction. Meanwhile, the ECA-Net channel attention mechanism is embedded into the feature extraction layer and PANet (Path Aggregation Network) to improve the recognition accuracy of the model for small targets. The experimental results show that the size of the improved model is reduced from 244 MB to 42 MB, which is only 17.3% of the original model. At the same time, the mAp of the improved model is 0.77% higher than that of the original model, reaching 95.4%. Moreover, the mAP compared with YOLOv5-s and YOLOX-s, respectively, is improved by 1.98% and 1.29%. Finally, the improved model is deployed into Jetson Xavier NX and run at a speed of 8.8 FPS, which is 4.3 FPS faster than the original model.

Edge computing can avoid the long-distance transmission of massive data and problems with large-scale centralized processing. Hence, defect identification for insulators with object detection models based on deep learning is gradually shifting from cloud servers to edge computing devices. Therefore, we propose a detection model for insulators and defects designed to deploy on edge computing devices. The proposed model is improved on the basis of YOLOv4-tiny, which is suitable for edge computing devices, and the detection accuracy of the model is improved on the premise of maintaining a high detection speed. First, in the neck network, the inverted residual module is introduced to perform feature fusion to improve the positioning ability of the insulators. Then, a high-resolution detection output head is added to the original model to enhance its ability to detect defects. Finally, the prediction boxes are post-processed to incorporate split object boxes for large-scale insulators. In an experimental evaluation, the proposed model achieved an mAP of 96.22% with a detection speed of 10.398 frames per second (FPS) on an edge computing device, which basically meets the requirements of insulator and defect detection scenarios in edge computing devices.

The failure of insulators may seriously threaten the safe operation of the power system, where the state detection of high-voltage insulators is a must for the normal and safe operation of the power system. Based on the data of insulators in aerial images, this work explored an enhanced particle swarm algorithm to optimize the parameters of the support vector machine. A support vector machine model was therefore established for the identification of the normal and defective states of the insulators. This methodology works with the structure minimization principle of SVM and the characteristics of particle swarm fast optimization. First, the aerial insulator image was segmented as a target by way of the seed region growth based on double-layer cascade morphological improvements, and then, HOG features plus GLCM features were extracted as sample data. Finally, an ameliorated PSO-SVM classifier was designed to realize insulator state identification. Comparisons were made between PSO-SVM and conventional machine learning algorithms, SVM and Random Forest, and an optimization algorithm, Gray Wolf Optimization Support Vector Machine (GWO-SVM), and advanced neural network CNN. The experimental results showed that the performance of the algorithm proposed in this paper touched the top level, where the recognition accuracy rate was 92.11%, the precision rate 90%, the recall rate 94.74%, and the F1-score 92.31%.

Transformers play an essential role in power networks, ensuring that generated power gets to consumers at the safest voltage level. However, they are prone to insulation failure from ageing, which has fatal and economic consequences if left undetected or unattended. Traditional detection methods are based on scheduled maintenance practices that often involve taking samples from in situ transformers and analysing them in laboratories using several techniques. This conventional method exposes the engineer performing the test to hazards, requires specialised training, and does not guarantee reliable results because samples can be contaminated during collection and transportation. This paper reviews the transformer oil types and some traditional ageing detection methods, including breakdown voltage (BDV), spectroscopy, dissolved gas analysis, total acid number, interfacial tension, and corresponding regulating standards. In addition, a review of sensors, technologies to improve the reliability of online ageing detection, and related online transformer ageing systems is covered in this work. A non-destructive online ageing detection method for in situ transformer oil is a better alternative to the traditional offline detection method. Moreover, when combined with the Internet of Things (IoT) and artificial intelligence, a prescriptive maintenance solution emerges, offering more advantages and robustness than offline preventive maintenance approaches.

Performing ultrasonic nondestructive testing experiments on insulators and then using machine learning algorithms to classify and identify the signals is an important way to achieve an intelligent diagnosis of insulators. However, in most cases, we can obtain only a limited number of data from the experiments, which is insufficient to meet the requirements for training an effective classification and recognition model. In this paper, we start with an existing data augmentation method called DBA (for dynamic time warping barycenter averaging) and propose a new data enhancement method called AWDBA (adaptive weighting DBA). We first validated the proposed method by synthesizing new data from insulator sample datasets. The results show that the AWDBA proposed in this study has significant advantages relative to DBA in terms of data enhancement. Then, we used AWDBA and two other data augmentation methods to synthetically generate new data on the original dataset of insulators. Moreover, we compared the performance of different machine learning algorithms for insulator health diagnosis on the dataset with and without data augmentation. In the SVM algorithm especially, we propose a new parameter optimization method based on GA (genetic algorithm). The final results show that the use of the data augmentation method can significantly improve the accuracy of insulator defect identification.

HSV-1 is a human pathogen that establishes a lifelong infection in the host. HSV-1 is transported by retrograde axonal transport to sensory neurons in the peripheral nervous system where latent viral genomes can reactivate. The resulting virus travels via anterograde axonal transport to the periphery and can cause clinical disease. CTCF insulators flank the LAT and IE regions of HSV-1 and during latency and maintain the integrity of transcriptional domains through a myriad of functions, including enhancer-blocking or barrier-insulator functions. Importantly, during reactivation, CTCF protein is evicted from the HSV-1 genome, especially from the CTRL2 insulator. CTRL2 is a functional insulator downstream of the 5′exon region of the LAT, so these results suggest that the disruption of this insulator may be required for efficient HSV-1 reactivation. To further explore this, we used a recombinant virus containing a deletion of the CTRL2 insulator (ΔCTRL2) in a rabbit ocular model of HSV-1 infection and induced reactivation. We show that, in the absence of the CTRL2 insulator, HSV-1 established an equivalent latent infection in rabbits, but those rabbits failed to efficiently reactivate from latency. Furthermore, we found a significant decrease in the expression of the gene Us9-, a gene that codes for a type II membrane protein that has been shown to be required for anterograde transport in neurons. Taken together, these results suggest that the functions of the CTRL2 insulator and Us9 activation in reactivating neurons are intrinsically linked through the regulation of a gene responsible for the axonal transport of HSV-1 to the periphery.

For the benefit of knowing the method of insulator deicing using hot water, this paper investigated the physical aspects of propagation and the spatial temperature distribution of a hot water jet in the air. The numerical calculation model for the water jet flow field was established and validated, and the temperature spatial variation rule and influencing factors of the water jet were analyzed. The results indicate that the water column breaks as the distance increases. The numerical calculation results and the experimental results of the water jet flow field were approximated. As the distance increases, the mass entrainment rate of the water jet gradually increases, and the normalized axis velocity decreases approximately linearly. The jet temperature rises with the rise in the initial temperature of the hot water, and the rising rate decreases with the increase in distance. The temperature of the water jet falls as the jet distance increases. With the rise in the outlet pressure, the temperature of the water jet drops slightly. The influence of environmental wind speed on the water jet temperature is more significant than ambient temperature—the water jet temperature increases as the nozzle diameter increases. The thorough deicing efficiency is most outstanding when the hot water output temperature is around 86 °C and the jet pressure is ~3.5 MPa.

Insulators are one of the many components responsible for the reliability of electricity supply as part of transmission and distribution lines. Failure of the insulator can cause considerable economic problems that are much greater than the insulator cost. When the failure occurs on the transmission line, a large area can be without electricity supply or other transmission lines will be overloaded. Because of the consequences of the insulator’s failure, diagnostics of the insulator plays a significant role in the reliability of the power supply. Basic diagnostic methods require experienced personnel, and inspection requires moving in the field. New diagnostic methods require online measurement if it is possible. Diagnostic by measuring the leakage current flowing on the surface of the insulator is well known. However, many other quantities can be used as a good tool for diagnostics of insulators. We present in this article results obtained on the investigated porcelain insulators that are one of the most used insulation materials for housing the insulator’s core. Leakage current, dielectric loss factor, capacity, and electric charge are used as diagnostic quantities to investigate porcelain insulators in different pollution conditions and different ambient relative humidity. Pollution and humidity are the main factors that decrease the insulator´s electric strength and reliability.

Defective insulators seriously threaten the safe operation of transmission lines. This paper proposes an insulator defect detection method based on an improved YOLOv4 algorithm. An insulator image sample set was established according to the aerial images from the power grid and the public dataset on the Internet, combining with the image augmentation method based on GraphCut. The insulator images were preprocessed by Laplace sharpening method. To solve the problems of too many parameters and low detection speed of the YOLOv4 object detection model, the MobileNet lightweight convolutional neural network was used to improve YOLOv4 model structure. Combining with the transfer learning method, the insulator image samples were used to train, verify, and test the improved YOLOV4 model. The detection results of transmission line insulator and defect images show that the detection accuracy and speed of the proposed model can reach 93.81% and 53 frames per second (FPS), respectively, and the detection accuracy can be further improved to 97.26% after image preprocessing. The overall performance of the proposed lightweight YOLOv4 model is better than traditional object detection algorithms. This study provides a reference for intelligent inspection and defect detection of suspension insulators on transmission lines.

Heparanase is an endo-β-glucuronidase that is best known for its pro-cancerous effects but is also implicated in the pathogenesis of various viruses. Activation of heparanase is a common strategy to increase viral spread and trigger the subsequent inflammatory cascade. Using a Single Nucleotide Polymorphisms (SNP)-associated approach we identified enhancer and insulator regions that regulate HPSE expression. Although a role for heparanase in viral infection has been noticed, the impact of HPSE functional SNPs has not been determined. We investigated the effect of cytomegalovirus (CMV) serostatus on the involvement of HPSE enhancer and insulator functional SNPs in the risk of acute graft versus host disease (GVHD) and granulocyte-colony stimulating factor related CD34+ mobilization. A significant correlation between the C alleles of insulator rs4364254 and rs4426765 and CMV seropositivity was found in healthy donors and patients with hematological malignancies. The risk of developing acute GVHD after hematopoietic stem cell transplantation was identified only in CMV-seropositive patients. A significant correlation between the enhancer rs4693608 and insulator rs28649799 and CD34+ cell mobilization was demonstrated in the CMV-seropositive donors. It is thus conceivable that latent CMV infection modulates heparanase regulatory regions and enhances the effect of functional SNPs on heparanase function in normal and pathological processes.

Successive flashover would result in carbonized tracking on insulator surface and cause deterioration to the insulation. Thus, investigation of the tracking can be beneficial in understanding flashover characteristics during long-term operation. In this paper, DC flashover was operated on the insulator, and the image of tracking after successive discharge were captured. Improved differential box-counting method (IDBM) was applied to analyze these images based on fractal theory. Weighted item was suggested during the counting procedure for rectangle image with margin covered by cut-size box. Fractal dimension of the tracking was calculated according to the suggested method. It is claimed that the suggested method could estimate the discharge propagation property and deterioration characteristics on the insulator surface. Moreover, IDBM showed advantages in image pre-processing and deterioration property revealed compared to traditional box-counting method attributing to the consideration of color depth. This image analysis method shows universality in dealing with tracking image and could provide additional information to flashover voltage. This paper suggested a potential approach for the investigation of discharge mechanism and corresponding deterioration in future research.

The HPSE gene encodes heparanase (HPSE), a key player in cancer, inflammation, and autoimmunity. We have previously identified a strong HPSE gene enhancer involved in self-regulation of heparanase by negative feedback exerted in a functional rs4693608 single-nucleotide polymorphism (SNP) dependent manner. In the present study, we analyzed the HPSE gene insulator region, located in intron 9 and containing rs4426765, rs28649799, and rs4364254 SNPs. Our results indicate that this region exhibits HPSE regulatory activity. SNP substitutions lead to modulation of a unique DNA-protein complex that affects insulator activity. Analysis of interactions between enhancer and insulator SNPs revealed that rs4693608 has a major effect on HPSE expression and the risk of post-transplantation acute graft versus host disease (GVHD). The C alleles of insulator SNPs rs4364254 and rs4426765 modify the activity of the HPSE enhancer, resulting in altered HPSE expression and increased risk of acute GVHD. Moreover, rs4426765 correlated with HPSE expression in activated mononuclear cells, as well as with CD3 levels and lymphocyte counts following G-CSF mobilization. rs4363084 and rs28649799 were found to be associated with CD34+ levels. Our study provides new insight into the mechanism of HPSE gene regulation and its impact on normal and pathological processes in the hematopoietic system.

By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.

In power inspection tasks, the insulator and spacer are important inspection objects. UAV (unmanned aerial vehicle) power inspection is becoming more and more popular. However, due to the limited computing resources carried by a UAV, a lighter model with small model size, high detection accuracy, and fast detection speed is needed to achieve online detection. In order to realize the online detection of power inspection objects, we propose an improved SSD (single shot multibox detector) insulator and spacer detection algorithm using the power inspection images collected by a UAV. In the proposed algorithm, the lightweight network MnasNet is used as the feature extraction network to generate feature maps. Then, two multiscale feature fusion methods are used to fuse multiple feature maps. Lastly, a power inspection object dataset containing insulators and spacers based on aerial images is built, and the performance of the proposed algorithm is tested on real aerial images and videos. Experimental results show that the proposed algorithm can efficiently detect insulators and spacers. Compared with existing algorithms, the proposed algorithm has the advantages of small model size and fast detection speed. The detection accuracy can achieve 93.8%. The detection time of a single image on TX2 (NVIDIA Jetson TX2) is 154 ms and the capture rate on TX2 is 8.27 fps, which allows realizing online detection.

Among the main causes of outdoor insulation failures is their poor specifications in terms of leakage distances. This happens when the selected criteria are unable to cope with all the stresses imposed by the changes in environmental pollutions. Therefore, it is important for utilities to fully understand the actual pollution characteristics of the service environment in which the insulators are operating. In this paper, the pollution severity and performance of some 13.2 kV ceramic insulators, sampled in different areas of a Canadian aluminum factory, are assessed. The investigations were performed taking into account the influence of air humidity. Various characteristics were investigated to assess the pollution levels of the insulators, such as equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD), surface resistance, and leakage current characteristics (density, 3rd harmonic amplitude, and phase). It was witnessed that the insulators, collected around the factory, were much more polluted in comparison to the initial expectation. The pollution level should not be considered static due to the environmental parameters’ dynamics. Lessons to learn: the reliability of an electrical grid is dependent on components whose own reliability is strongly affected by external factors, of which there is often a poor awareness. If care is not taken to re-evaluate the post-installation pollution levels of the insulators, the light may simply turn out!

Predicting the electrical breakdown of polymers is critical for certifying the endurance and lifetime of high voltage power equipment. Since various factors contribute nonlinearly to the breakdown phenomena of polymer insulators, it is difficult to assess the impact of each factor independently. In this study, we numerically analyzed the breakdown phenomenon because of the ramp rate of the DC voltage applied to a polymer insulator, low-density polyethylene (LDPE), using the finite element method (FEM). To predict the breakdown initiation, we analyzed the relaxation time of the conduction current through the insulator as a significant indicator. The bipolar charge transport (BCT) model was used to analyze the charge behavior within the LDPE, and the breakdown voltage was predicted by incorporating the molecular displacement model. This analysis was conducted for a wide range of ramp rates from 10 to 1500 V/s. The current density was calculated using two different methods, namely the energy and average methods, and the results were compared with each other. The results of the numerical model were further verified by comparing with those from experiments reported in the literature.

Transposable elements (TEs) are constitutive components of both eukaryotic and prokaryotic genomes. The role of TEs in the evolution of genes and genomes has been widely assessed over the past years in a variety of model and non-model organisms. Drosophila is undoubtedly among the most powerful model organisms used for the purpose of studying the role of transposons and their effects on the stability and evolution of genes and genomes. Besides their most intuitive role as insertional mutagens, TEs can modify the transcriptional pattern of host genes by juxtaposing new cis-regulatory sequences. A key element of TE biology is that they carry transcriptional control elements that fine-tune the transcription of their own genes, but that can also perturb the transcriptional activity of neighboring host genes. From this perspective, the transposition-mediated modulation of gene expression is an important issue for the short-term adaptation of physiological functions to the environmental changes, and for long-term evolutionary changes. Here, we review the current literature concerning the regulatory and structural elements operating in cis provided by TEs in Drosophila. Furthermore, we highlight that, besides their influence on both TEs and host genes expression, they can affect the chromatin structure and epigenetic status as well as both the chromosome’s structure and stability. It emerges that Drosophila is a good model organism to study the effect of TE-linked regulatory sequences, and it could help future studies on TE−host interactions in any complex eukaryotic genome.

The deterioration of the cement that bonds the cap, pin, and porcelain shell of the porcelain insulators can be observed by three-dimensional computed tomography (3D-CT), a non-destructive test. When porcelain insulators are used for a long duration, the size of the pores existing in the cement increase as the cement expands due to the alkali-silica reaction (ASR). It is possible to visually confirm the corrosion of caps and pins. The degree of corrosion is divided into four noticeable stages (I−IV), and deterioration of cement includes one of the four stages of corrosion. The standard time of replacement of insulator vs. continued use is presented. As a result of X-ray diffraction (XRD) analysis, Fe is oxidized to Fe2O3 at 36, 43, 54, and 70 degrees. The corrosion in cement is caused by factors including the value of pH, oxygen content, and temperature. For the caps and pins, although a zinc coating is applied to prevent the corrosion of iron, corrosion of zinc is caused by substances present in the external environment. When the zinc coating begins to corrode, the corrosion of the Fe component of the cap and pin accelerates, causing mechanical/electrical problems.

The detection of insulators in power transmission and transformation inspection images is the basis for insulator state detection and fault diagnosis in thereafter. Aiming at the detection of insulators with different aspect ratios and scales and ones with mutual occlusion, a method of insulator inspection image based on the improved faster region-convolutional neural network (R-CNN) is put forward in this paper. By constructing a power transmission and transformation insulation equipment detection dataset and fine-tuning the faster R-CNN model, the anchor generation method and non-maximum suppression (NMS) in the region proposal network (RPN) of the faster R-CNN model were improved, thus realizing a better detection of insulators. The experimental results show that the average precision (AP) value of the faster R-CNN model was increased to 0.818 with the improved anchor generation method under the VGG-16 Net. In addition, the detection effect of different aspect ratios and different scales of insulators in the inspection images was improved significantly, and the occlusion of insulators could be effectively distinguished and detected using the improved NMS.

In this paper, an artificial pollution test is carried out to study the leakage current of porcelain insulators. Fractal theory is adopted to extract the characteristics hidden in leakage current waveforms. Fractal dimensions of the leakage current for the security, forecast and danger zones are analyzed under four types of degrees of contamination. The mean value and the standard deviation of the fractal dimension in the forecast zone are calculated to characterize the differences. The analysis reveals large differences in the fractal dimension of leakage current under different contamination discharge stages and degrees. The experimental and calculation results suggest that the fractal dimension of a leakage current waveform can be used as a new indicator of the discharge process and contamination degree of insulators. The results provide new methods and valid indicators for forecasting contamination flashovers.

Due to the complex structure of insulators, it is difficult to use parameters such as icicle length or ice thickness of an insulator to directly characterize the degree of icing of an insulator. Rotating conductors are widely used in monitoring icing degree on insulators, but the relationship between ice degree on the insulator and the rotating conductor has not been verified. In this paper, the water droplets collision coefficient α1 was put forward to characterize icing degree, and a new numerical calculation model where α1 on different regions of an insulator is calculated was proposed. Combining the freezing fraction α3 of the insulator and rotating conductor, the equivalent relationship of ice weight between insulator and rotating conductor can be established, which was afterwards verified through the icing tests. The test results indicate that ice weight on an insulator increases linearly with the increase of ice weight on the rotating conductor, and the model proposed in this paper can reflect actual results more accurately than previous models. In such cases, the method of using ice weight on a rotating conductor to predict that on an insulator based on the model proposed in this paper could be widely adopted.

Icing can adversely influence electric power system security. Two main issues are caused by icing: the overload of transmission lines, and the reduction in the insulation ability of the insulators. Most previous research has focused on the flashover characteristics of ice-covered insulators, but research on the icing process of the insulator is seriously lacking. Considering the effect of icing shape, the outer airflow field of an insulator was calculated and the local collision efficiencies of water droplets (β1) were investigated according to the Lagrange algorithm. The simulation showed that the values of β1 on the insulator edge and rod are much higher than on the insulator surface, and both were significantly influenced by the wind speed and median volume diameter (MVD) of the water droplets. Based on thermal balance equations, a dynamic dry-growth icing model was established. Using the natural icing conditions of Xuefeng Mountain (China) as an example, validation experiments were conducted on a composite insulator and the climate parameters measured by multi-cylinders were used to model the icing shape and mass. The results indicate that high wind speed and low temperature increase icing rate; the icing was mainly concentrated on the windward side and the greatest horizontal thickness was generally on the insulator edge. The dry-growth model had an average error lower than 25% for icing thickness and an average error lower than 20% for icing mass, which were affected by icing roughness.

Energized insulator’s surface contamination is greatly affected by its electric field properties. However, few reports about the electric field influencing mechanism on the pollution deposition have been presented. In this paper, the coupling-physics model of a three-unit XP-160 insulator string was established, and the particles’ deposition process was simulated by using the finite element method. The effects of the electric field on the pollution particles’ motion were analyzed. Research results indicate that the closer to the insulator string, the larger the intersection angle θ between the electric force and its horizontal component. The main function of the electric field on insulator contamination is that it changes the pollution particles’ vertical moving speed, thus accelerating the particles’ deposition process. The particle capture coefficient ratio ck can be used to reflect the pollution degree discrepancy between DC-energized and non-energized insulators. In this paper the ratio ck of the DC-energized to non-energized condition is in the range of 1.04 to 1.98, very close to the field measurement results of the DC pollution ratio k.

Accurate and timely detection of insulator flashover on power transmission lines is of paramount importance to power utilities. Most available solutions mainly focus on the exploitation of the flashover mechanism or the discharge area detection, rather than the identification of a damaged area due to flashovers using captured aerial images. To this end, this paper proposes a multi-saliency aggregation-based porcelain insulator flashover fault detection approach. The target area of the insulator is determined using the Faster-Pixelwise Image Saliency by Aggregating (F-PISA) algorithm based on the color and structural features. The color model can be established based on the color feature of the damaged areas on the insulator surface, and hence the damaged area can be identified. Based on the information obtained above, the contour information can be extracted. With the preceding process, the fault location can be confirmed with a good accuracy. The performance of the proposed detection approach is assessed through a comparative study with other available solutions. The numerical result demonstrates that the suggested solution can detect the insulator flashover with improved performance in terms of the average detection rate and average efficient detection rate. Additional analysis is carried out to evaluate its robustness and real-time performance, which confirms its deployment feasibility in practice.

The cleaning effect of heavy rain (the rainfall reaches 5 mm every day) on surface contamination of insulators is more effective than dew, fog, mist, and other light rain conditions which can initiate leakage currents and increase the likelihood of flashover. It is well understood that heavy rain can wash away contamination from the surface of high voltage (HV) insulators and thereby reduce the risk of pollution flashover. This study examines the cleaning effect of natural wetting conditions on HV insulators on four 500 kV transmission lines in Hunan Province, China. Historical meteorological data, monthly equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) measurements taken over a period of five years were analyzed to investigate the relationship between rainfall intensity and insulator cleaning. The measured data show that the ESDD/NSDD changes with the seasonal variation, which accumulates in dry season (January–April, about 117–122 days) and is washed off in the wet season (June–October, about 118–127 days). According to the measured data, the ESDD and NSDD on the surface of insulators were affected by the rainfall intensity (in the dry season it is about 1 mm/day and in the wet season it is about 5 mm/day). Based on a comparison of the four study sites, we propose a mathematical model to show the functional relationship between rainfall intensity and insulator self-cleaning capability. The mathematical model’s coefficient of determination (R2) is greater than 0.9 and the effective rate of self-cleaning capability reaches 80%.

Ice flashover threatens the security and reliability of power transmission. However, the ice flashover mechanism of insulators remains poorly understood. This study analyses water droplet freezing and ion distribution in ice layer under alternating electric field. It also investigates ion migration during icing process of insulator string under alternating electric field and its effects on insulator flashover. Results showed that the average freezing time of water droplets was related to electric field strength. The extent of ion migration during freezing decreased with increasing electric field strength. The maximal melting water conductivity of the ice layer and icicle of the insulator formed under energized condition was higher than the corresponding freezing water conductivity but lower than that under non-energized condition. Furthermore, the hanging location of each insulator significantly affected the melting water conductivity of the ice layer. The surface conductivity of the ice layer increased because of the conductive ion migration in freezing water during freezing, which was an important factor that decreased the flashover voltage of the ice-covered insulator. The existence of alternating electric field would impact the extent of ion migration. This study may serve as a reference for updating prediction flashover model of ice-covered insulators during the melting period.

A novel OLED (organic light emitting diode) lighting panel, which uses a special layout design, can reduce the photolithography cycles and process costs and is more reliable. It only needs two steps of photolithography cycles, which include an ITO (InSnO compound transparent oxide) pattern and insulator pattern. There is no need for the metal bus pattern of the ordinary design. The OLED device structure is a type of red–green–blue (RGB)-stacked emitting layer that has a good color index and greater adjustability, which improves the performance of the device. This novel design has the same equipment and material requirement compared to the ordinary design, and it is very beneficial in terms of high volume and low-cost production. It uses a hyper driving method because the entire OLED lighting panel is divided into many sub-emitting units; if one of the sub-emitting units is burned out, it has no effect on the adjacent sub-emitting unit, so the reliability is markedly better than the ordinary design.

Natural polluted insulator surfaces are always coated with various kinds of soluble constituents, and those constituents affect flashover performance differentially. Currently, this fact is not considered either in laboratory experiments or field pollution degree measurements, causing the existing insulation selection method to be deficient. In this paper, a systematic study on insulator flashover voltage gradient correction involving different types of soluble pollution constituents is presented. Using a typical type glass insulator as the sample, its flashover tests, polluted by typical soluble chemicals (NaCl, NaNO3, KNO3, NH4NO3, MgSO4, Ca(NO3)2 and CaSO4), were carried out. Then, the flashover gradient correction was made by combining the flashover performance of each soluble constituent, the equivalent salt deposit density (ESDD) contribution of the seven constituents, and the saturation performance of CaSO4. The correction agreed with the flashover test results of insulator polluted by three types of soluble mixture. Research results indicate that the flashover gradient correction method proposed in this paper performs well in reducing the calculating error. It is recommended to carry out component measurements and flashover gradient correction to better select outdoor insulation configuration.

Retroviral vector gene therapy is a promising approach to treating HIV-1. However, integrated vectors are mutagens with the potential to dysregulate nearby genes and cause severe adverse side effects. Leukemia has already been a documented severe adverse event in gene therapy clinical trials for the treatment of primary immunodeficiencies. These side effects will need to be reduced or avoided if retroviral vectors are to be used clinically for HIV-1 treatment. The addition of chromatin insulators to retroviral vectors is a potential strategy for reducing adverse side effects. Insulators have already been effectively used in retroviral vectors to reduce genotoxicity in pre-clinical studies. Here, we will review how insulators function, genotoxicity in gene therapy clinical trials, the design of insulated retroviral vectors, promising results from insulated retroviral vector studies, and considerations for the development of insulated retroviral treatment vectors for HIV-1 gene therapy.