Insulator Detection Research Articles

Flaw detection of railway catenary insulator based on DP-YOLOv5 algorithm with bright and dark channel enhancement

Abstract In order to ensure the timely detection of safety hazards in overhead transmission lines with railroad conductors and improve the accuracy of night insulator defect detection, this paper proposes the DP-YOLOv5 algorithm with dark and light channel enhancement optimization. It improves the night insulator image quality by introducing the dark and light channel enhancement algorithm, builds a lightweight network by combining the DP-BS module, and adds the Shuffle Attention module to enhance the feature extraction and ensure detection accuracy. At the same time, the EC-Loss loss function is used to optimize the prediction frame adjustment, accelerate the model convergence, and improve detection efficiency and accuracy. The simulation results show that the accuracy of DP-YOLOv5 is 95.3%, the recall is 94.8%, the average accuracy is 95.5%, and the FLOPs are 219.3. Compared with YOLOv5, the mapped value is improved by 0.9%, the F1 is improved by 1%, and the model parameter and FLOPs are reduced by 48.8% and 50.8%, respectively.

L-YOLOv8s: an improved YOLOv8s based on lightweight for defect insulator detection

L-YOLOv8s: an improved YOLOv8s based on lightweight for defect insulator detection

The novel methods of insulation detection based on Adaptive Levenberg–Marquardt algorithm and Third-Order Variable Forgetting Factor Recursive Least Squares-Decouple algorithm

Electric vehicles (EVs) are central to the future of automotive development, with high-voltage insulation performance critical for operational safety. Existing insulation detection methods face challenges such as limited scope, low accuracy, poor interference resistance, and slow response. This study introduces two insulation detection models based on the unbalanced bridge method and low-frequency signal injection, analyzing their theoretical effectiveness and confirming superior detection capability in the unbalanced bridge method. Furthermore, to address feedback voltage waveform issues in this method, an adaptive Levenberg–Marquardt (ALM) algorithm is proposed to prevent the divergence typically seen in traditional approaches. Additionally, a decoupling algorithm utilizing a Third-order Variable Forgetting Factor Recursive Least Squares (TVFF-Decouple) simplifies algorithm complexity significantly while enabling anomaly detection. Finally, AEKF and SRCKF algorithms were used for observation, identifying an optimal combination that reduces noise interference effectively. Simulations and bench tests demonstrate that the proposed methods swiftly and accurately detect positive and negative insulation resistances and equivalent Y capacitance under various conditions.

Insulation Detection Algorithm for Gloves Based on Improved YOLOv8n

With the rapid development of China's industry, the safety problems in the process of electrical operation are highly concerned by the electrical industry. In order to reduce the incidence of electrical safety accidents, it is very necessary to wear insulating gloves for electric power construction, which is also of great significance to ensure the safety of electric power production. However, the small target of insulating gloves and the complexity of the electrician operation site, which undoubtedly increases the difficulty of identifying the algorithm model of insulating gloves. For this problem, insulation glove target detection algorithm based on YOLOv8n improvement model. Through many experiments, YOLOv8n was selected as the basic framework of the insulating glove detection algorithm. In order to make the model more accurately locate and identify the targets of interest, the efficient coordinate attention mechanism Coordinate Attention was inserted into backbone to improve the accuracy of the model. The introduction of PConv in necbricks reduces redundant computation and memory access for a more efficient extraction of spatial features. After the improved YOLOv8n algorithm, the mAP is increased from the initial 96.4% to 98.6%, which provides a good reference value for the subsequent research of the target detection algorithm of insulating gloves.

Decision Tree Clusters: Non-destructive detection of overheating defects in porcelain insulators using quantitative thermal imaging techniques

Porcelain insulators are subject to performance deterioration during operation under the influence of complex environments, thus increasing the occurrence of flashover accidents. Regular inspection of insulator condition is of great significance for the stable operation of power grids. Therefore, we propose a non-destructive detection method of insulator overheating defects using quantitative thermography. It can be used for defect detection of porcelain insulators with internal overheating defects. In this study, the relationship between the insulating properties of porcelain insulators and the heating anomalies was first analyzed. Then, Decision Tree Clusters (DTC) algorithm is used to extract the spatial temperature feature information to achieve the defective insulator location capture. Finally, Submodular-pick Local Interpretable Model-agnostic Explanations (SLIME) is used for model decision visualization to verify the feasibility of the technique. The experimental results show that DTC not only ensures the accuracy of insulator long-distance detection, but also realizes the quantitative detection of insulators.

Research on Lightweight Method of Insulator Target Detection Based on Improved SSD.

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.

Target localization and defect detection of distribution insulators based on ECA‐SqueezeNet and CVAE‐GAN

AbstractInsulators, as typical equipment for distribution networks, provide good electrical insulation between live conductors and earth. Timely and accurate detection is essential for insulator detection issues. However, as the complexity of neural networks increases, the detection efficiency is often lower. Therefore, this paper proposes a fast insulator positioning and defect detection method. Firstly, for insulator target localization, the SqueezeNet network is improved using ECA attention mechanism. In addition, to address the issue of low defect detection accuracy, a joint algorithm has been proposed. The integration of convolutional variational autoencoder (CVAE) and generative adversarial network (GAN) solve their own shortcomings due to different image focus angles. The target localization accuracy reaches 94.30%, and the defect detection accuracy reaches 89.60%. It solves the problems of difficulty in locating small targets in a large field of view and inaccurate detection due to a small number of abnormal samples. This method has been tried and tested in practical distribution network systems.

Research on insulator image segmentation and defect recognition technology based on U‐Net and YOLOv7

AbstractThis study focuses on aerial images in power line inspection, using a small sample size and concentrating on accurately segmenting insulators in images and identifying potential “self‐explode” defects through deep learning methods. The research process consists of four key steps: image segmentation of insulators, identification of small connected regions, data augmentation of original samples, and detection of insulator defects using the YOLO v7 model. In this paper, due to the small sample size, sample expansion is considered first. A sliding window approach is adopted to crop images, increasing the number of training samples. Subsequently, the U‐Net neural network model for semantic segmentation is used to train insulator images, thereby generating preliminary mask images of insulators. Then, through connected region area filtering techniques, smaller connected regions are removed to eliminate small speckles in the predicted mask images, obtaining more accurate insulator mask images. The evaluation metric for image recognition, the dice coefficient, is 93.67%. To target the identification of insulator defects, 35 images with insulator defects from the original samples are augmented. These images are input into the YOLO v7 network for further training, ultimately achieving effective detection of insulator “self‐explode” defects.

Insulator detection based on FA‐YOLO network with improved feature extraction ability

AbstractUnmanned aerial vehicle insulator detection that aims to recognize defective insulators from transmission lines has made significant progress in recent years. However, it still faces challenges, such as the complex background of aerial images and the small memory of unmanned aerial vehicles. This paper proposes a refined insulator detection algorithm that integrates the attention mechanism in YOLOv8 to improve the feature extraction ability. Specifically, this paper introduces a fast vision transformers structure in the you only look once (YOLO) v8 backbone section to enhance feature extraction by capturing local and global features. Additionally, the global attention mechanism is incorporated in the neck for additional feature extraction by merging comprehensive spatial and channel information into the output. Furthermore, we amalgamate depth‐wise convolution, graph convolution, and residual operation in the global attention mechanism module. This design can mitigate the issues of gradient vanishing or exploding and meanwhile enhance the distinction between spatial attention and channel attention. The proposed model is then applied to a public dataset and a set of real images from a specific power station, and the detection results show that it outperforms many competitors in terms of accuracy, efficiency, and memory size.

Self-Powered Analytical Strips Based on Evaporation-Driven Generator for ESDD Detection of Insulator in Electrical Equipment

Self-Powered Analytical Strips Based on Evaporation-Driven Generator for ESDD Detection of Insulator in Electrical Equipment

Failure type and failure level detection of insulators according to monitored leakage current

Due to the ever-increasing growth of electric energy consumption, the production of high-quality, reliable and high-reliability electricity is very important. Therefore, it is essential to have distribution and sub-transmission networks with a good reliability factor. In power distribution and sub-transmission lines, it is necessary to somehow isolate the conductors under voltage from the towers, and insulators are used for this purpose. These insulators have two main tasks. One of the main tasks of insulators is to isolate (insulate) the line conductor from the body of the tower. The insulators must be able to isolate the high voltages of the lines from the body of the tower without having a leakage current, and on the other hand, the insulators must be able to withstand the mechanical forces resulting from the weight of the conductors and the applied forces caused by wind and ice. Also, leakage current is one of the important parameters for condition monitoring of insulators in power grid lines. Failure to inspect the insulation for contamination and health conditions will lead to insulator failure and will cause faults in the electrical system. Therefore, it is very important to monitor the condition of the insulator. Based on this, in this paper, according to the data related to leakage current and also according to the introduced wear out function, a procedure for measuring the condition of insulators has been obtained. Finally, the condition of each insulator will be determined according to the defined indicators. Also, the failure level of each monitored data will be obtained using sensitivity analysis.

Lightweight Oriented Detector for Insulators in Drone Aerial Images

Due to long-term exposure to the wild, insulators are prone to various defects that affect the safe operation of the power system. In recent years, the combination of drones and deep learning has provided a more intelligent solution for insulator automatic defect inspection. Positioning insulators is an important prerequisite step for defect detection, and the accuracy of insulator positioning greatly affects defect detection. However, traditional horizontal detectors lose directional information and it is difficult to accurately locate tilted insulators. Although oriented detectors can predict detection boxes with rotation angles to solve this problem, these models are complex and difficult to apply to edge devices with limited computing power. This greatly limits the practical application of deep learning methods in insulator detection. To address these issues, we proposed a lightweight insulator oriented detector. First, we designed a lightweight insulator feature pyramid network (LIFPN). It can fuse features more efficiently while reducing the number of parameters. Second, we designed a more lightweight insulator oriented detection head (LIHead). It has less computational complexity and can predict rotated detection boxes. Third, we deployed the detector on edge devices and further improved its inference speed through TensorRT. Finally, a series of experiments demonstrated that our method could reduce the computational complexity of the detector by approximately 49 G and the number of parameters by approximately 30 M while ensuring almost no decrease in the detection accuracy. It can be easily deployed to edge devices and achieve a detection speed of 41.89 frames per second (FPS).

Distribution network insulator detection based on improved ant colony algorithm and deep learning for UAV

Distribution network insulator detection based on improved ant colony algorithm and deep learning for UAV

Contrasting YOLOv7, SSD, and DETR on Insulator Identification under Small-sample Learning

Background:: Daily inspections of insulators are necessary because they are indispensable components for power transmission lines. Using deep learning to monitor insulators is a newly developed method. However, most deep learning-based detection methods rely on a large training sample set, which consumes computing resources and increases the workload of sample labeling. The selection of learning models to monitor insulators becomes problematic. Objective:: Through comparative analysis, a model suitable for small-sample insulator learning is found to provide a reference for the research and application of insulator detection. Methods:: This paper compares some of the latest deep learning models, YOLOv7, SSD, and DETR, for insulator detection based on small-sample learning. The small sample here means that the number of samples and their proportion to the total sample are relatively small. Two public insulator image sets, InsulatorDataSet with 600 insulator images and Transmission-line-pictures (TLP) with 1230 insulator images in the natural background are selected to test the performance of these models. Results:: Tests on two public insulator image sets, InsulatorDataSet and TLP, show that the recognition rates of YOLOv7, DETR, and SSD are arranged from high to low. The DETR and the YOLOv7 have stable performance, while the SSD lacks stable performance in terms of the learning time and recognition rate. Conclusion:: The in-domain and cross-domain scenario tests show that YOLOv7 is more suitable for insulator detection under small-sample conditions among the three models.

Occluded Insulator Detection System Based on YOLOX of Multi-Scale Feature Fusion

Occluded Insulator Detection System Based on YOLOX of Multi-Scale Feature Fusion

FPGA-Based VFF-RLS Algorithm for Battery Insulation Detection in Electric Vehicles

As the adoption of electric vehicles (EVs) continues to rise, attention has switched to ensuring the safety of EV operations. The exponential growth in battery technology over the past several years has changed the face of energy storage and sparked a revolution in several industries. The degradation of battery insulation during regular use is a significant concern. The high voltage (HV) and current levels in HV electric vehicles pose a significant electrical threat.The advancement of electric vehicle technology has led to an increasing presence of HV electric equipment throughout the vehicle. The insulation strength and early health status detection of the batteries are essential in ensuring safety in EVs. This paper studies the different insulation detection techniques and the development of adaptive filter (AF) algorithms based on field-programmable gate arrays (FPGAs) for insulation detection. FPGAs are amongst the most accurate and fast detection techniques among all the insulation detection techniques used so far in electric vehicles. This study proposes an FPGA-based VFF-RLS algorithm for effectively implementing insulation detection in EVs. The experimental test results using FPGAs demonstrate that the proposed method can rapidly monitor changes in insulation resistance (IR). The VFFRLS-based FPGA technique works sufficiently well to reduce errors when dealing with variations in voltage and resistance conditions at the battery terminals.

A novel method for electric vehicle insulation detection based on the extended Kalman filter algorithm

Addressing the critical need for enhanced safety in the burgeoning electric vehicle market, this study presents a novel insulation detection method based on the Extended Kalman Filter (EKF) algorithm. To resolve the conflict between the response speed and detection accuracy of the insulation detection system caused by Y-capacitors, the Levenberg–Marquardt (L-M) algorithm is employed to effectively estimate the parameters of the feedback voltage model developed for an unbalanced electrical bridge. The state equation of the insulation testing system has been constructed, and the EKF algorithm is applied innovatively to monitor insulation resistance and Y-capacitance, demonstrating superior anti-interference capabilities. Simulation experiments have underscored the significant contributions of the L-M algorithm in expanding the detection scope of the system. Bench tests confirmed the ability of the approach to monitor changes in insulation resistance and Y-capacitance rapidly and accurately. Under normal conditions, the maximum relative error for insulation resistance measurement is 1.56%, with a response time of 1.5 s.

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.

Ground fault detection in substation DC system based on improved differential current method

DC system is the main electrical equipment and control signal system in the substation, and its stability and reliability are directly related to whether the power system can operate safely. This paper firstly introduces the common methods of insulation detection of DC power system in substation and its advantages and disadvantages, and proposes the differential current method based on unbalanced bridge, i.e., using the combination of unbalanced bridge and leakage current sensor to detect the insulation condition of DC system, which makes up for the defects of the traditional differential current method that can only detect the faults of simple branch circuits, and realizes the detection of multi-branch grounding under complex situations. Finally, the feasibility of this method is verified by building a simulation model through simulink.

A Latency-Aware End-Edge Cooperative Insulator Detection Method with High Energy Efficiency and Accuracy

A prerequisite to ensure the stability of the power supply system is suitable functioning of transmission line equipment. However, the increasing deployment of transmission lines in modern power systems has introduced significant challenges to line inspection. While deep learning-based image detection techniques have shown promise in improving the efficiency and accuracy of insulator detection, they often require substantial computational resources and energy. This limitation hinders the consistent guarantee of accuracy and real-time performance on resource-constrained drones. To address this issue, this paper investigates the co-optimization problem of energy consumption and analytic accuracy in insulator image detection on unmanned aerial vehicles (UAVs). We propose a latency-aware end-edge cooperative insulator detection task offloading scheme with high energy efficiency and accuracy that aims to achieve optimal performance. Initially, we conducted an experimental analysis to examine the influence of input image resolution on the accuracy and latency of the CNN-based insulation detection model. Subsequently, we develop a model that takes into account the latency, analytic accuracy and energy consumption for image detection task offloading. Finally, we formalized a nonlinear integer optimization problem and designed a particle swarm optimization (PSO)-based task offloading scheme to optimize task accuracy and energy consumption while adhering to latency constraints. Extensive experiments validated the effectiveness of the proposed end-edge cooperative insulator detection method in optimizing accuracy and energy consumption.