Insulator defect detection algorithm based on a lightweight network

As a special insulation device, insulators play an important role in aerial transmission lines. It can be easily influenced by the wind load, snow load and wire swing in severe weather. The fault of the insulator will seriously affect and destroy the safety and normal operation of the entire transmission line. Therefore, in the operation and inspection of the transmission line, the detection of insulator defects must be strengthened. Since the insulators are constructed in a complex environment and the background of the images collected by air is also extremely complicated, traditional defect detection methods rely on specific scenarios for specific analysis and the robustness is weak. Changes in scenes, changes in UAV’s shooting angles, and changes in lighting conditions will all make it difficult for the model to get good inspection results. This paper proposes an algorithm for locating and detecting insulator defects based on an enhanced YOLO model. Due to the electromagnetic interference during the power inspection of the UAV, there is a certain amount of noise in the image of the aerial insulator. At the same time, in order to solve the problem of few shots which leads to training difficulty, this paper uses a variety of data augmentation, including affine transformation, Gaussian blur processing, gray scale transformation, brightness and contrast transformation and random erasing of pixels, and then the augmentation data is used to train different YOLO models. The results show that the detection algorithm based on the YOLO model meets the robustness and accuracy of insulator defect detection, and augmented experiment results are better than those of the conventional YOLOs in power line insulator defect detection tasks.

Insulators are the basis of the stable operation of transmission lines, and it is particularly important to regularly detect the insulation performance of insulators. After several years of research, contactless space electric field detection technology for degraded insulators, based on an unmanned aerial vehicles (UAV) platform, has developed rapidly. However, the existing technology is only limited to the typical vertical/horizontal state research category and cannot apply to complex operating conditions such as attitude disturbance and path tilt. This paper builds a simulation model of insulators at a voltage level of 220 kV by COMSOL finite element software and analyzes the space electric field distribution characteristics of degraded insulators and the influence of different surface conditions, attitude disturbance, path tilt, etc., on the space electric field distribution of insulators. To reduce the error of UAV detection under complex operating conditions of insulators, a method for detecting the electric field of degraded insulators based on sensitive insulators is proposed and verified by experiments. According to the research results, surface conditions of insulators have little influence on their space electric field, while the attitude disturbance and path tilt of UAVs have a great influence on the electric field of insulators. Sensitive insulators at a level of voltage below 220 kV are generally located at the low-voltage end. The principles of sensitive insulators are combined with the space electric field measurement method to measure the space electric field distribution, thus effectively judging whether there are degraded insulators in the string. The method specified in the paper greatly improves the accuracy and detection efficiency of the contactless electric field detection of insulators, which is of great significance for the promotion of the electric field detection method of insulators in practical applications.

With the rapid development of autonomous driving technology, traditional object detection algorithms often face challenges such as the trade-off between model parameters and recognition accuracy, insufficient detection accuracy, and slow detection speed when dealing with vehicle and pedestrian detection tasks in complex scenarios. To address these challenges, we propose a novel lightweight real-time driving scene detection network, FEEP-YOLO. Based on the YOLOv8 algorithm, our network not only maintains high detection accuracy but also achieves faster training speed and fewer model parameters. FEEP-YOLO employs a lightweight network, Faster Net, to significantly reduce model parameters, thereby accelerating detection speed and better meeting real-time requirements. To further improve detection accuracy, we introduce an EMA attention module into the network and optimize the bounding box loss function, adopting the EIOU loss function to enhance target features and reduce background interference. Additionally, to detect small objects more accurately, we add feature layers rich in semantic information to the detection end. Experimental results demonstrate that FEEP-YOLO outperforms YOLOv8 in terms of both detection speed and accuracy on the KITTI and SODA datasets, while significantly reducing the number of model parameters and model size. FEEP-YOLO achieves high detection speed while improving detection accuracy and reducing computational resource consumption, meeting the practical application requirements under limited computing power, memory space and power consumption. The key contribution of FEEP-YOLO lies in its ability to balance accuracy speed, and efficiency, making it a robust solution for real-time autonomous driving applications in complex environments.

To tackle the issue of low detection accuracy in insulator images caused by intricate backgrounds and small defect sizes, as well as the requirement for real-time detection on embedded and mobile devices, this research introduces the YOLOv5s-KE model. Integrating multiple strategies, YOLOv5s-KE aims to boost detection accuracy significantly. Initially, an enhanced anchor generation method utilizing the K-means++ algorithm is proposed to generate more appropriate anchor boxes for insulator defects. Moreover, an attention mechanism is integrated into both the backbone and neck networks to enhance the model’s capacity to focus on defect features and resist interference. To improve the detection of small defects, the EIoU loss function is implemented in place of the original CIoU loss function. In order to meet the real-time detection needs on embedded and mobile devices, the model is further refined through the integration of Ghost convolution for lightweight feature extraction and a linear transformation to reduce the computational burden of standard convolution. A channel pruning strategy is deployed to optimize the sparsely trained network, diminishing redundancy, and improving model generalization. Additionally, the CARAFE operator replaces the original upsampling operator to minimize model parameters and elevate detection speed. Experimental outcomes demonstrate that YOLOv5s-KE achieves a detection accuracy of 92.3% on the Chinese transmission line insulator dataset, marking a 5.2% enhancement over the original YOLOv5s. The streamlined version of YOLOv5s-KE achieves a detection speed of 94.3 frames per second, indicating an improvement of 30.1 frames per second compared to the original model. Model parameters are condensed to 9.6 M, resulting in a detection accuracy of 91.1%. This study underscores the precision and efficiency of the proposed approach, suggesting that the advanced strategies explored introduce novel possibilities for insulator defect detection.

The timely detection of insulator defects in transmission lines is vital for ensuring social production and people’s livelihoods. Aiming to solve the problem of the low accuracy of insulator defect detection in current detection models, this study improves the YOLO11n model and proposes an insulator defect detection model, FPFS-YOLO, that integrates FasterNet and an attention mechanism. In this study, to mitigate parameter redundancy in the backbone of the YOLO11n model, the FasterNet lightweight network was introduced, and some convolution was embedded into the shallow network to enhance its feature extraction ability. To solve problems such as insufficient attention to important features and the low detection ability of small defects in the YOLO11n model network, the ParNet attention mechanism was added, along with a small-defect detection layer, which improved the detection accuracy of the model. Finally, in order to alleviate the computational redundancy caused by these additions, the C3k2_faster module and the PSP-Head detection head were introduced. These amendments further improved the accuracy of the model network in detecting insulator defects while simultaneously reducing its computational redundancy. The experimental results show that the improved FPFS-YOLO model achieved a 91.5% mAP@50 and a 56.6% mAP@0.5-0.95, increases of 3.1% and 1.2%, respectively, while the precision and recall reached 93.2% and 86.4%, increases of 1.5% and 4.2%, respectively. The FPFS-YOLO model achieved a higher detection accuracy than the YOLO11n model and thus could be widely applied in the detection of insulator defects.

Insulators are critical components of transmission lines, and defective insulators pose a serious threat to the safety of power supply systems. Timely detection of these defects is crucial to prevent catastrophic consequences for human lives and property. However, insulator defects are often small and easily affected by the noise of rain, fog, sunlight, dirt, and other pollutants, making detection challenging. We observe that diffusion models learn data distribution by progressively introducing noise and subsequently performing denoising. The progressive denoising mechanism can naturally simulate the randomness of environmental noise. Based on this observation, we treat the localization of insulator defects as a denoising-based recovery process, where the true defect bounding boxes are progressively reconstructed from noisy representations. To this end, we propose a novel diffusion-based Insulator Defect Detector (IDDet) that is specifically designed to handle complex environmental noise. IDDet introduces noise to the true bounding boxes to generate noisy target boxes with random distributions and is then trained to recover the true bounding boxes from these noisy representations through a residual denoising diffusion mechanism. For the inference stage, IDDet refines the defect location from a random noise bounding box by gradually removing the noise, ultimately achieving the task of precisely locating the defect in the image. Experimental results show that IDDet significantly improves detection capability in noisy environments, achieving the best mean average precision (mAP) of 92.3%, confirming the feasibility and effectiveness of our approach.

Bolts are important components on transmission lines, and the timely detection and exclusion of their abnormal conditions are imperative to ensure the stable operation of transmission lines. To accurately identify bolt defects, we propose a bolt defect identification method incorporating an attention mechanism and wide residual networks. Firstly, the spatial dimension of the feature map is compressed by the spatial compression network to obtain the global features of the channel dimension and enhance the attention of the network to the vital information in a weighted way. After that, the enhanced feature map is decomposed into two one-dimensional feature vectors by embedding a cooperative attention mechanism to establish long-term dependencies in one spatial direction and preserve precise location information in the other direction. During this process, the prior knowledge of the bolts is utilized to help the network extract critical feature information more accurately, thus improving the accuracy of recognition. The test results show that the bolt recognition accuracy of this method is improved to 94.57% compared with that before embedding the attention mechanism, which verifies the validity of the proposed method.

As power grid structure becomes increasingly complex, the impact of the operation status of key substation equipment on the safe and stable operation of the power grid is gradually increasing. Aiming at the problems of dense targets and occlusion recognition difficulties in the complex background of substations, an improved you-only-look-once (YOLO) v4 insulator defect detection algorithm is proposed. A large number of insulator and defect datasets were collected from the Internet. We use MobileNet-v2 to replace the backbone network of YOLOv4, and in view of the problem that the accuracy of YOLOv4 target detection model is not high for small targets or dense targets, convolutional block attention module (CBAM) attention mechanism is added to the backbone network to enhance the feature extraction ability, and ultra-lightweight subspace attention module (ULSAM) is added to the detection head to learn the cross-channel information. Experiments based on InDataset show that the insulator defect detection algorithm based on deep learning proposed achieves a high detection accuracy and detection speed; the mean average precision of the improved YOLOv4 model is 87.48%, and the recall rate of insulator defects reaches 79.84%, which meets the requirements of robustness and accuracy of insulator defect detection in real scenarios.

Bolts are widely used in transmission lines and are key connecting parts of transmission lines. Transmission lines have been affected by the natural environment for a long time, and bolts are prone to frequent vibration leading to loosening or falling off. Bolt loosening endangers the safe and stable operation of transmission lines. This paper analyses the loosening mechanism of the transmission line screw connection, carries out the transverse vibration contrast test of the common bolt anti-loosening measures, and develops the integrated self-locking double nut for the hidden danger of the transmission line bolt loosening, which has good anti-loosening performance, convenient installation, and the conditions for engineering popularization and application.

Hubei Power Grid is located in an area with frequent lightning activities, and the minefield level in most areas is above C1. Lightning strikes cause a large proportion of line trip accidents, which seriously affects the safe and stable operation of transmission lines. In order to understand the characteristics of lightning activity and tripping in Hubei Province, further analyze lightning damage and realize the planning of transmission lines and the formulation of lightning protection measures, the lightning activities and lightning trips of Hubei Power Grid in 2018 are analyzed in this work, and conducts a detailed analysis of the transmission lines of three important channels. The results show that the lightning activity of Hubei Power Grid in 2018 is extremely strong, and the temporal and spatial distribution is highly concentrated, and the nature of lightning is shielding failure strike; the number of trips of 500 kV AC and ±500 kV lines due to lightning strikes is much greater than that of ±800 kV and 1000 kV lines and the auto-reclosure rate is high; the number of lines, topography, span, tower type and tower height will affect the line tripping, and lightning protection needs to be strengthened.

The loose failure of transmission tower bolts may lead to structural instability and safety risks, so effective monitoring methods are crucial to the stable operation of transmission lines. The purpose of this study is to explore the effectiveness of vibration signal technology in monitoring the loose bolts of transmission tower. Based on vibration theory, the principle of bolt loosening of transmission tower is analyzed, vibration signal data is collected by means of vibration exciter and optical fiber vibration sensor, and then the monitoring test of transmission tower loosening fault is carried out. The obtained test results show that the time domain waveform is significantly different before and after excitation, and the wavelength after excitation has a significant mutation, increasing from 1550 nm to 1553 nm, and slowly decreasing to the original wavelength, which also means that the transmission tower bolt loosening fault monitoring system has a good monitoring ability and can be used for vibration measurement. According to these monitoring results, the conclusions can be obtained as follows: first, the frequency domain data amplitude changes before and after loosening can be used to judge whether the bolt is loose, so as to achieve the monitoring purpose; Second, the strength of the vibration signal is large, the vibration signal change caused by the loosening of the bolt is submerged, and the installation of excitation at the sensor should be avoided to ensure that the monitoring is not disturbed by external factors. The research provides a new technical way for real-time monitoring of loose bolt fault of transmission tower, which has practical value and popularization prospect.

Smart grid emphasizes identifiability, controllability, unified coordination ability, safety and stability of power grids. In the process of building smart grids, the requirements of transmission lines are mainly reflected in two aspects: state monitoring and transmission capacity of line. Realizing the monitoring of transmission line status, could timely discover hidden failure of transmission line and reduce the number of power failure, ensuring safe and stable operation of transmission lines. The excavation of the transmission capacity is reflected in timely adjustment of the current capacity to maximize the capacity of the transmission line. This paper proposed from the demand of smart grid construction, and combine the output status information of smart substation to realize the real-time status monitoring of transmission lines, and then supporting the condition maintenance of transmission lines and improving the management level of transmission assets. At the same time, through the on-line monitoring of the line temperature and the meteorological environment of the transmission line, comprehensively collect data for calculation analysis and verification to achieve the purpose of dynamic capacity-increase of the transmission line.

Aluminum conductor steel reinforced (ACSR) will produce broken strand defects after a period of operation, which seriously affects the safe and stable operation of transmission lines. In this paper, a two-dimensional LGJ-240/30 type ACSR broken strand defective heat transfer model is proposed to study the corresponding relationship between the number of broken strands of ACSR and the temperature rise of the defective backside and the load current. The model is simulated using COMSOL Multiphysics to obtain a fitting formula for the number of broken strands under natural convection conditions. The test platform is built under natural conditions to verify the feasibility and accuracy of the formula. The results show that, using the formula proposed in the article, the number of broken strands of ACSR can be identified more accurately under the condition that the load current and backside temperature are known, and improve the efficiency of infrared inspection.

Operation accidents caused by deteriorated insulators occur from time to time, which poses a direct threat to the safe and stable operation of transmission lines. Much research has been done at home and abroad on the degradation mechanism of deteriorated insulators, the electric field distribution characteristics of insulator strings and the influence of deteriorated insulators on the space electric field, but there is little research on the influence of three-phase electrification on the space electric field of insulator strings. Therefore, this paper studies the simulation and detection of electric field distribution of deteriorated insulators in three-phase transmission lines. First, the difference between three-phase electrification and single-phase electrification on the space electric field of insulator strings is simulated and analyzed, and the influence of deteriorated insulators on the space electric field distribution of insulator strings under three-phase electrification is studied. Second, based on simulation results, a detection method for deteriorated insulators in three-phase overhead trans-mission lines is proposed, and a non-contact space electric field measurement device based on Unmanned Aerial Vehicle (UAV) is developed. Finally, a Unmanned Aerial Vehicle inspection system is used to test the transmission lines in combination with an electric power department, and the simulation results and the effectiveness of the proposed detection method are verified. Results show the electric field distribution of insulator strings is obviously different between three-phase electrification and single-phase electrification, and when the detection distance is 300 mm, the proposed detection method and device can effectively identify deteriorated insulators in three-phase transmission lines.

The paper proposes a method to construct a model for transmission line galloping prediction using machine learning algorithms to address the transmission line galloping problem, which can result in transmission line loss and pose a greater risk to the safety of electricity in society. First, to reduce sensor noise interference, a unilateral sliding time window is used for micro-meteorological data correction, and then gray correlation analysis method and the specific gravity method are used to obtain the influence weights of micro-meteorological elements. The galloping prediction models are constructed using six algorithms, with the GA-BP algorithm model and the SVM algorithm model having better prediction effects based on performance metrics. The GA-BP-SVM combined model is constructed on this basis, and all of its performance metrics are optimal. This model's prediction accuracy in both galloping and no galloping states reaches 95.5%; the probability of correct prediction when predicted as galloping reaches 95.1%; the probability that actual galloping can be predicted reaches 92.5%. The F1-score of the combined model reaches 0.938, which indicates that it has the best prediction effect. The prediction method described in the paper is accurate and practical, and operation and maintenance personnel can flexibly develop inspection strategies and anti-galloping measures based on the prediction results to ensure the safe and stable operation of transmission lines.