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

Ahmed, Faiyaz; Mohanta, J. C.; Sanyal, Alok

doi:10.1016/j.epsr.2022.108199

Cited by 49 publications

(16 citation statements)

References 36 publications

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“…Using literature [3] and literature [4] as comparative methods, the accuracy of different methods in identifying the operating status of deteriorated insulators was verified. The results are shown in Table 1 1 shows that the method proposed in this paper can effectively achieve the output of identification results for the operating status of deteriorated insulators, with a high accuracy rate of 96.28% on average, while other methods have lower identification accuracy rates of 91.9% and 90.86% on average, respectively.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

See 1 more Smart Citation

A method for identifying the operating state of deteriorated insulators based on partial discharge signal reconstruction algorithm and spatial electric field distribution law

Li,

Wang,

Yang

et al. 2024

J. Phys.: Conf. Ser.

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In order to improve the accuracy of identifying the working status of degraded insulators and thereby contribute to preventive maintenance and repair, this proposes a method for identifying the operating status of deteriorated insulators based on a partial discharge signal reconstruction algorithm and spatial electric field distribution law. Firstly, the partial discharge signal of the deteriorated insulator is obtained through capacitive sensor technology and clustering analysis algorithm. Then, the mapping features between the partial discharge signal and the electric field distribution are extracted using the SN-EMD algorithm to determine the spatial electric field distribution on the surface of the insulator. Finally, a state recognition model is established based on the hidden Markov model to identify the operating status of deteriorated insulators. The experimental results show that the method designed in this paper has superior performance in terms of accuracy.

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Section: Experimental Results and Analysismentioning

confidence: 99%

“…In [3], a detection method based on autonomous vision is proposed, which uses a four-rotor aircraft to collect images of transmission towers and utilizes deep learning for insulator defect detection. To address the issue of insufficient training data, a medium-sized insulator dataset was created.…”

Section: Introductionmentioning

confidence: 99%

A method for identifying the operating state of deteriorated insulators based on partial discharge signal reconstruction algorithm and spatial electric field distribution law

Li,

Wang,

Yang

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Finally, the challenges and future directions of this eld are put forward, and the summary and outlook are made. [12][13][14] Fig. 2 shows the number of power line inspection with deep learning publications in recent years.…”

Section: Introductionmentioning

confidence: 99%

Research on vision-based UAV power line detection technology

Wang

2023

Preprint

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In order to ensure the reliability and safety of the power grid operation, the power company will carry out regular inspection of the transmission line network. The traditional inspection method is generally manual inspection by foot patrol, which is not only slow but also time-consuming and laborious. In recent years, with the development of unmanned aerial vehicle(UAV) and unmanned vehicle control technology and the improvement of inspection accuracy, UAV technology has been widely used in the intelligent inspection of power grid. In order to ensure the reliability and safety of power grid operation in the complex and huge power grid system, in order to find the fault of transmission line as soon as possible and give feedback, The UAV detection technology with image recognition technology has become an urgent need for the development of power system. The vision-based UAV power line detection technology has a wide range of application prospects. In this paper, the relevant literature in recent years is reviewed extensively, and the status quo of vi-sion-based UAV detection technology is reviewed. This paper summarizes the existing power line detection methods, the system structure of UAV power line detection and the image recognition technology of data acquisition, and focuses on the development of transmission line data processing technology based on deep learning, in order to provide a starting point for researchers to develop automatic autonomous intelligent inspection of transmission line system. Finally, the development of this field and the next possible challenges are discussed, and the future is prospected.

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“…Given the complexity of the power system transmission line environment, 3 traditional detection methods are slow and have low accuracy. Consequently, the process of detecting and addressing insulator defects has become fraught with challenges and obstacles 4 – 6 …”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the process of detecting and addressing insulator defects has become fraught with challenges and obstacles. [4][5][6] In the realm of traditional image recognition methods, the feature extraction process heavily relies on manual design, 7 which lacks the much-needed generalization ability and robustness. As computer vision technology has evolved, the use of deep learning for image recognition has revolutionized the field, surpassing the performance of traditional recognition algorithms in terms of accuracy and real-time efficiency.…”

Section: Introductionmentioning

confidence: 99%

Improving insulator fault detection with effective-YOLOv7 network

Ye,

Liu,

et al. 2023

J. Electron. Imag.

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Ensuring reliable and steady operation of power equipment is paramount to safeguard the livelihoods and labor of the populace. However, traditional detection techniques face obstacles when adapting to the intricate background of transmission lines, leading to innumerable incorrect and missed detections. To resolve these issues, an improved YOLOv7 transmission line insulator defect recognition algorithm has been introduced. An anchor frame, matching the size of the transmission line fault, has been constructed using K-means++, followed by a convolutional block attention module to enhance the ability to extract features. Next, the wise-IoU loss function has been incorporated, providing a gradient gain allocation strategy, thereby enhancing the positioning performance and speed of the model. Finally, the SiLU activation function has been replaced with meta-ACON to enhance the adaptive feature capability of the YOLOv7 network. Experimental results have shown that the proposed method has an average accuracy (mAP) of 91.8%, and the detection accuracy for transmission lines can be improved to 98.8%. This model resolves the persisting issues of erroneous and missing detections by addressing the traditional technical difficulties of detecting complex backgrounds and defects with insufficient accuracy.

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

Cited by 49 publications

References 36 publications

A method for identifying the operating state of deteriorated insulators based on partial discharge signal reconstruction algorithm and spatial electric field distribution law

A method for identifying the operating state of deteriorated insulators based on partial discharge signal reconstruction algorithm and spatial electric field distribution law

Research on vision-based UAV power line detection technology

Improving insulator fault detection with effective-YOLOv7 network

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