Inspection Of Insulators Research Articles

Research on resistance detection robot for UHV tension insulator string

Insulator inspection robots can realize automatic and rapid inspection of insulators of ultra-high voltage transmission lines, which is an important guarantee equipment for the continuous safe operation of the national power grid. Aiming at the needs of ultra-high voltage insulator string inspection, an insulator robot is designed to realize efficient and stable insulator string motion and insulator detection, and the main work includes proposing a new insulator string motion robot configuration and realizing its string motion and inspection functions in the form of a physical prototype. It has been proved that the robot has a large motion load and strong motion performance, and can be adapted to the complex working conditions consisting of a variety of insulators within a certain size range.

Autonomous Site Inspection of Power Transmission Line Insulators with Unmanned Aerial Vehicle System

The inspection of overhead power transmission line and assets is an essential aspect to improve the overhead power transmission efficiency and to ensure an uninterrupted power supply. This article has mainly focused on the progression and fabrication of indigenous quadcopter/Unmanned Aerial Vehicle (UAV) for carrying autonomous operations in a coordinated movement along the overhead transmission towers for capturing the images and videos of transmission insulators and assets. A custom based dataset of power line insulators is created by using the quadcopter for overcoming the data scarcity and to perform Deep Learning (DL) assessment for (i) inadequate data for training and (ii) power line insulator detection and faults. The experimental results showcase that, the suggested DL architecture identifies power line insulators and associated faults, such as cracks, broken disk and missing top caps etc. With a detection speed of 56.8 frames/sec and an accuracy of 94.1%, the proposed DL technique has much promise for intelligent examination of power grid insulators. Ecological Footprint assessment of different power line inspection methods are also examined in this study.

Increasing the efficiency of the bactericide use at PS-4 of "Ozenmunaigas" JSC

Background: The study is devoted to increasing the efficiency of bactericidal treatment of sediments at the oil fields of Ozenmunaigas JSC. Calculations were carried out to assess the capability of heating the formation and the pipeline, hydraulic heat loss when injecting a bactericide, and the efficiency of mixing the reagent with water. The analysis revealed the ineffectiveness of mixing the bactericide with water at low temperatures and rates of inhibitor introduction.
 Aim: The purpose of the study was to identify solutions to overcome the problems caused by the increased content of hydrogen sulfide in the fields of Ozenmunaigas JSC. This included addressing the temperature issues during bactericide delivery and increasing the rate of inhibitor injection.
 Materials and methods: Heat loss calculations were carried out using the UniSim program for both tank heating and input lines, as well as an analysis of bactericide mixing inside the pipeline using the ANSYS program.
 Results: The results of the study led to specific recommendations and measures that could mitigate the problems associated with temperature and rate of injection. These included measures such as heating and insulation, temperature control, regular inspection of thermal insulation, and selection of a new reagent with improved mixing properties at lower temperatures. It is also recommended to install reagent flow and temperature sensors along with power supply control.
 Conclusion: The implementation of the proposed measures will effectively solve the problems associated with high levels of hydrogen sulfide. This approach will ensure a smooth and safe flow of bactericide and help maintain the operating efficiency of the affected facilities.

Inspection of power transmission line insulators with autonomous quadcopter and SSD network

Inspection of power transmission line insulators with autonomous quadcopter and SSD network

A lightweight YOLOv7 insulator defect detection algorithm based on DSC-SE.

As the UAV(Unmanned Aerial Vehicle) carrying target detection algorithm in transmission line insulator inspection, we propose a lightweight YOLOv7 insulator defect detection algorithm for the problems of inferior insulator defect detection speed and high model complexity. Firstly, a lightweight DSC-SE module is designed using a DSC(Depthwise Separable Convolution) fused SE channel attention mechanism to substitute the SC(Standard Convolution) of the YOLOv7 backbone extraction network to decrease the number of parameters in the network as well as to strengthen the shallow network's ability to obtain information about target features. Then, in the feature fusion part, GSConv(Grid Sensitive Convolution) is used instead of standard convolution to further lessen the number of parameters and the computational effort of the network. EIoU-loss(Efficient-IoU) is performed in the prediction head part to make the model converge faster. According to the experimental results, the recognition accuracy rate of the improved model is 95.2%, with a model size of 7.9M. Compared with YOLOv7, the GFLOPs are reduced by 54.5%, the model size is compressed by 37.8%, and the accuracy is improved by 4.9%. The single image detection time on the Jetson Nano is 105ms and the capture rate is 13FPS. With guaranteed accuracy and detection speed, it meets the demands of real-time detection.

MGFNet: A Progressive Multi-Granularity Learning Strategy-Based Insulator Defect Recognition Algorithm for UAV Images

Due to the low efficiency and safety of a manual insulator inspection, research on intelligent insulator inspections has gained wide attention. However, most existing defect recognition methods extract abstract features of the entire image directly by convolutional neural networks (CNNs), which lack multi-granularity feature information, rendering the network insensitive to small defects. To address this problem, we propose a multi-granularity fusion network (MGFNet) to diagnose the health status of the insulator. An MGFNet includes a traversal clipping module (TC), progressive multi-granularity learning strategy (PMGL), and region relationship attention module (RRA). A TC effectively resolves the issue of distortion in insulator images and can provide a more detailed diagnosis for the local areas of insulators. A PMGL acquires the multi-granularity features of insulators and combines them to produce more resilient features. An RRA utilizes non-local interactions to better learn the difference between normal features and defect features. To eliminate the interference of the UAV images’ background, an MGFNet can be flexibly combined with object detection algorithms to form a two-stage object detection algorithm, which can accurately identify insulator defects in UAV images. The experimental results show that an MGFNet achieves 91.27% accuracy, outperforming other advanced methods. Furthermore, the successful deployment on a drone platform has enabled the real-time diagnosis of insulators, further confirming the practical applications value of an MGFNet.

A High-Precision Insulation Inspection Method Based on the Double-Arm Unbalanced Bridge for Electric Ships

In order to ensure the safe navigation of electric ships, it is especially important to detect the insulation status between the ship’s high-voltage system and the ship’s hull in real time. In this paper, a real-time insulation resistance (IR) detection method based on the double-arm unbalanced bridge method was proposed for the problem that the IR calculation results of electric ships have large errors. The detection principle was described, and an experimental test platform was built. The influence law of each branch resistance and voltage on the IR measurement error in this circuit was studied to provide a reference for the parameter selection of the double-arm unbalanced bridge and the effect of temperature on the measurement results was also considered. Besides, the Extended Kalman filtering (EKF) algorithm was used to reduce the influence of voltage fluctuation on the insulation detection error. The results show that the insulation testing method could accurately measure the IR, and the insulation testing error was less than 2.4%, and the error was about 0.18% after processing with the EKF algorithm. This proves that this paper improves the measurement accuracy of IR from three aspects: circuit design of insulation testing, bridge parameter selection and filtering algorithm.

Multi-Objects Recognition and Self-Explosion Defect Detection Method for Insulators Based on Lightweight GhostNet-YOLOV4 Model Deployed Onboard UAV

In order to meet the application requirements on unmanned aerial vehicle (UAV) for daily inspection of insulators in overhead grid transmission lines, a lightweight GhostNet-YOLOV4 model is proposed to identify the insulators objects and detect their self-explosion defect meanwhile. In the proposed model, the lightweight GhostNet with full convolutional attention module (C-SE) model is embedded into the YOLOV4 as backbone feature extraction network, and the ordinary convolution and Mish activation function of enhanced feature extraction network in GhostNet-YOLOV4 are replaced by the depth separable convolution and ReLU6 activation function respectively. Meanwhile, the combination methods of data augmentation, label smoothing, K-Means algorithm, adaptive cosine annealing learning rate, and Focal Loss function are adopted to optimize the model. Finally, the class activation mapping (CAM) is used to visually analyze for the identification process of the proposed model. The example analysis results show that the object recognition accuracy mAP of the proposed model is 99.50% for insulator objects and self-explosion defect, and the image processing speed FPS is 2.53 frames/s. The proposed model meets the requirements of lightweight, detect accuracy and speed for UAV application.

ASSESSMENT OF THE GENERAL LEVEL OF DAMAGEABILITY TO INSULATION COATINGS OF STEEL UNDERGROUND GAS DISTRIBUTION PIPELINES

The article presents the results of analysis of long-term dynamics of damage of protective coatings of distribution gas pipelines revealed in the process of operation. The applied methodology of complex instrumental inspection and diagnostics of insulation is highlighted. The estimation of the general level of damageability of protective coverings on the basis of the value of specific density of defects was carried out. The received results testify to an insignificant degree of wear and absence of signs of approaching the limit state of gas pipelines' insulating coatings, including those being in operation for more than 40 years. This is an additional argument in favor of revising the normative service life of steel distribution gas pipelines towards increase.

High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5

As a key component in overhead cables, insulators play an important role. However, in the process of insulator inspection, due to background interference, small fault area, limitations of manual detection, and other factors, detection is difficult, has low accuracy, and is prone to missed detection and false detection. To detect insulator defects more accurately, the insulator defect detection algorithm based on You Only Look Once version 5 (YOLOv5) is proposed. A backbone network was built with lightweight modules to reduce network computing overhead. The small-scale network detection layer was increased to improve the network for small target detection accuracy. A receptive field module was designed to replace the original spatial pyramid pooling (SPP) module so that the network can obtain feature information and improve network performance. Finally, experiments were carried out on the insulator image dataset. The experimental results show that the average accuracy of the algorithm is 97.4%, which is 7% higher than that of the original YOLOv5 network, and the detection speed is increased by 10 fps, which improves the accuracy and speed of insulator detection.

A new link-legged insulator inspection robot

• The link-legged insulator inspection robot is based on the Chebyshev linkage. • The robot can climb both vertical and horizontal insulator strings. • The robot can carry other executing mechanisms, such as cleaning equipment. • The design is verified in our laboratory by a prototype. Due to the increasing labor power shortage and safety requirements in insulator string inspection, inspection robots have been a primary alternative to replace humans in such an arduous environment. This paper presents a newly designed link-legged insulator inspection robot based on the Chebyshev linkage, which can inspect deteriorated insulators and climb both vertical and horizontal insulator strings. The robot consists of three identical climbing mechanisms, which use two sets of the Chebyshev linkage to achieve the full-time climbing function. An insulator inspecting device is attached to achieve the inspection function. The top of the robot is designed as a flat surface to make it able to carry other executing mechanisms, such as cleaning or vision equipment. This paper also covers a basic robot trajectory planning method, which aims to make the robot climb smoothly and overcome the shortcoming of wide velocity variation in the direction of movement. We have made a prototype of the robot and conducted an experiment in the laboratory, which proves that the robot can climb and inspect both vertical and horizontal insulator strings as desired.

Terahertz non-destructive testing of power generator bars with a dielectric waveguide antenna

AbstractTerahertz technologies for non-destructive testing (NDT) are continuing to find their way into the industrial sector in the context of very specific inspection tasks. Part of this development is the capability to adapt terahertz systems in such a way that they can meet the sometimes harsh challenges and requirements of real-world industrial scenarios. One such scenario is the inspection of components with limited available measurement space. In particular, we show here the terahertz NDT inspection of the mica insulation of generator bars of turbogenerators at power plants, where an early on-site detection of defects and cracks in the insulation can be crucial, but where only few centimeters of space between adjacent bars are available. To address this problem, we have developed a measurement system combining a 100 GHz all-electronic terahertz transceiver with a low-loss dielectric waveguide antenna with 90 degree tip. We achieve sub-wavelength image resolution by scanning the waveguide antenna's tip over the surface of the generator bars in a near-field measurement setup. Employing a frequency-modulated continuous wave technique, we obtain depth-resolved, volumetric terahertz images of the objects under test. We discuss here the implementation and performance of the implemented measurement system for terahertz NDT inspection. keywords: terahertz, non-destructive testing, dielectric waveguides, frequency-modulated continuous wave, millimeter waves, power generators

Uncertainty-aware accurate insulator fault detection based on an improved YOLOX model

The surveillance and inspection of power line insulators, which act as essential components for the connection and insulation of power lines, play pivot roles in the daily maintenance of the power grid system since the failure of power line insulators could cause abrupt power cuts and accidents. Vision-assisted Unmanned Aerial Vehicle (UAV) technology has recently become an efficient and economical solution for automatic insulator fault inspection and demonstrated considerable accuracy with deep learning-based detection methods. However, current detection methods cannot predict and handle the uncertainty of insulator detection, and their capabilities are limited. This paper presents a novel deep learning-based methodology, namely YOLOD, to address the uncertainty issue by applying a Gaussian prior to the detection heads of YOLOX, the current state-of-the-art model of compact object detectors, for both bounding box regression and corresponding uncertainty estimation. Then, the estimated uncertainty scores are utilized to refine the bounding box prediction and further improve the robustness of the detection. Finally, in the comprehensive experiments, the proposed YOLOD model outperforms other benchmark models on a public insulator dataset and achieves the highest average precision (73.9%), which is 2.1% higher than that of YOLOX. Thus, the effectiveness and superiority of the proposed method for robust insulator defect inspection are validated.

Insulator and Burst Fault Detection Using an Improved Yolov3 Algorithm

Insulators play an important role in the operation of outdoor high-voltage transmission lines. However, insulators are installed in outdoor environments for long periods and thus failures are inevitable. It is necessary to conduct timely insulator inspection and maintenance. In this paper, an improved Yolov3 target detection network (Yolov3-CK) is proposed in order to achieve higher detection accuracy and speed. First, Yolov3-CK uses the CIOU loss function instead of the mean square error loss function from Yolov3. Second, the Yolov3-CK model uses cluster analysis of the priori box via the k -means++ algorithm to obtain a priori box size that is more suitable for the detection of insulators and their burst faults. Finally, we use a dataset obtained by performing data enhancement on the China power line insulator dataset to train and test the data-enhanced Yolov3-CK model. The mean precision of Yolov3-CK reaches 91.67% with 47.9 frames processed per second. Yolov3-CK provides better detection accuracy and a higher processing rate than Faster RCNN, SSD, and Yolov3. Therefore, the Yolov3-CK model is more suitable for the detection of insulators and their burst faults.

Multi-Geometric Reasoning Network for Insulator Defect Detection of Electric Transmission Lines.

To address the challenges in the unmanned system-based intelligent inspection of electric transmission line insulators, this paper proposed a multi-geometric reasoning network (MGRN) to accurately detect insulator geometric defects based on aerial images with complex backgrounds and different scales. The spatial geometric reasoning sub-module (SGR) was developed to represent the spatial location relationship of defects. The appearance geometric reasoning sub-module (AGR) and the parallel feature transformation (PFT) sub-module were adopted to obtain the appearance geometric features from the real samples. These multi-geometric features can be fused with the original visual features to identify and locate the insulator defects. The proposed solution is assessed through experiments against the existing solutions and the numerical results indicate that it can significantly improve the detection accuracy of multiple insulator defects using the aerial images.

Inspection and identification of transmission line insulator breakdown based on deep learning using aerial images

The traditional methods of overhead power transmission line inspections are mostly unsuited as the height of transmission towers is too high and wide. Detection and inspection of insulators in aerial images with cluttered backgrounds is a challenging task for autonomous inspections. This manuscript mainly focuses on the development of autonomous Unmanned Aerial Vehicles (UAV/Quadcopter) that can hover over the transmission towers and capture images and videos by following pre-determined waypoints. To accomplish this, authors propose a new autonomous vision-based inspection that uses a Quadcopter as primary source of data, aerial images as the main source of information, and Deep Learning (DL) as the backbone analysis for inspection and focused on (i) insufficient training data, (ii) detection of insulators and their defects. A medium sized dataset of insulators for training and detection is created to overcome data insufficiency. The experimental results shows that the proposed deep learning architecture successfully identifies the anomalies of insulator such as, cracks, missing top caps and broken disk etc. The detection accuracy of the proposed deep learning algorithm can reach up to 93.5% with a detection speed of 58.2 frames/sec. The proposed DL algorithm has a promising potential towards smart inspection of insulators in power grids.

Research on Small Sample Data-Driven Inspection Technology of UAV for Transmission Line Insulator Defect Detection

Insulators are important safety devices on high-voltage transmission lines. An insulator inspection system based on UAVs is widely used. Insulator defect detection is performed against two main engineering problems: 1. The scarcity of defect images, which leads to a network overfitting problem. 2. The small object detection, which is caused by the long aerial photography distance, and the low resolution of the insulator defect area pictures. In this study, firstly, the super-resolution reconstruction method is used to augment the dataset, which can not only solve the overfitting problem but also enrich the image texture features and pixel values of defect areas. Secondly, in the process of insulator defect detection, a two-stage cascading method is used. In the first stage, the rotated object detection algorithm is used to realize the object location of insulator strings, and then images of the identified insulators are cropped to reduce the proportion of the background area in defect images. In the second stage, YOLO v5 is used for the detection of insulator caps that are missing defects. The method proposed shows good detection effect on the self-built training set which contains only 85 images captured from real inspection environments. The method has practical industrial application value.

Faster R-Transformer: An efficient method for insulator detection in complex aerial environments

Insulator inspection is important for high-voltage transmission line safety. However, existing object-detection algorithms cannot complete the task robustly in complex aerial environments. To improve the performance of the detection task, this paper proposes an algorithm, called Faster R-Transformer, which combines the advantage of Convolutional neural networks and the self-attention mechanism for aerial insulator detection. First, the D-ResNet performs feature extraction to obtain a larger receptive field. Then, the RPN module is used for region recommendation, and the Align Pooling module is used for region Pooling. Finally, the self-attention mechanism is adopted in the proposed region to perform local region feature weighting. The Faster R-Transformer was evaluated in complex aerial environments, and the evaluation showed that the proposed method was capable of detecting insulators of various sizes under different illuminations and viewpoints. We also explored hyperparameters of Faster R-Transformer to meet the requirements of various scenarios.

Inspection of High Voltage Insulators with a Deep Learning Model

Inspection of High Voltage Insulators with a Deep Learning Model

Impact of PWM Voltage Waveforms in High-Speed Drives: A Survey on High-Frequency Motor Models and Partial Discharge Phenomenon

The insulation system’s dielectric of the electric motor is very often subjected to severe electrical stress generated by the high dv/dt seen at the machine’s terminals. The electrical stress and high reflected wave transient overvoltage are even more evident in case of high-speed machines fed by high-frequency (HF) converters featuring very fast wide-bandgap devices. They are promoting the occurrence of partial discharges and consequently accelerate ageing. As this is serious issue and the main cause of the drive failure, it is important to analyse and characterise the surges at the motor terminals. Several HF models of motors have been proposed in the literature for this purpose. This article presents a survey on HF motor models, which is crucial in understanding and studying the most critical parameter identification and overvoltage mitigation techniques. Moreover, it offers a comparison of the models’ main features as well as a comparison with the experimental voltage waveform at motor terminals. A general overview of the partial discharge (PD) phenomenon is also provided, as it is favoured by HF operation and together with HF motor modelling provides key insights to the insulation ageing issue. In particular, an analysis of the effects of PWM waveform affecting insulation is given, as well as useful methods for developing strategies for the inspection and maintenance of winding insulation.