Research on edge intelligent recognition method oriented to transmission line insulator fault detection

Deng, Fang−Ming; Xie, Zhongxin; Mao, Wei; Li, Bing; Shan, Yun; Wei, Baoquan; Zeng, Han

doi:10.1016/j.ijepes.2022.108054

Cited by 48 publications

(31 citation statements)

References 31 publications

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“…On that basis, insulator defects were successfully detected under complex background. Deng [38] et al replaced the backbone feature extraction network Cross Stage Paritial Network (CSPdarkNet) [39] in the YOLOV4 algorithm with the lightweight network MobileNetv3 [40] and optimized it to obtain faster detection speed, and achieved better detection results on such defects as insulator self-detonation. Zhang et al [41] proposed a morphological processing and deep learning-based insulator image detection.…”

Section: Detection Based On Deep Learningmentioning

confidence: 99%

DBFN: Double Branch Fusion Network for Vital Components and Defect Detection of Transmission Line

Chu

Yang

2023

Advcd Theory and Sims

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A double branch fusion network is proposed based on unmanned aerial vehicle (UAV) inspection images to increase the detection accuracy of vital components and defects in transmission lines. The backbone feature extraction network comprises a combination of a convolutional neural network (CNN) and a Transformer network. To be specific, the CNN should extract local information, and the Transformer network is responsible for the extraction of global information. Besides, global information and local information have semantic differences, while resulting in feature aliasing after fusion. To solve this problem, a multiscale convolution module and a multiscale pooling module are proposed to solve semantic differences and feature aliasing through the interaction between two types of information. In general, the enhanced feature extraction network comprises a residual‐like convolution module, which can reduce the loss of detailed information (e.g., edge contours) and further extract high‐level semantic information from the deep network. Besides, it performs feature fusion in multiple regions in the enhanced feature extraction network, such that the multi‐scale adaptability of the neural network is effectively enhanced. Last, the fused feature information at different scales is decoded, and the final detection results are yielded.

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Section: Detection Based On Deep Learningmentioning

confidence: 99%

DBFN: Double Branch Fusion Network for Vital Components and Defect Detection of Transmission Line

Chu

Yang

2023

Advcd Theory and Sims

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“…Transmission line image data is collected by UAV, and automatic detection is performed by deep learning target detection network [8]. A UAV equipped with an intelligent module embedded in YOLOv4 was used to perform the task of insulator self-detonation fault detection [9]. However, these methods can only detect the outside of high-voltage transmission line equipment, and cannot reflect the specific conditions inside the equipment.…”

Section: Related Workmentioning

confidence: 99%

Live line strain clamp's DR image anomaly detection based on unsupervised learning

Haoliang

Jia

Yuting

et al. 2023

IET Generation Trans & Dist

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Due to the high‐risk working environment of high‐voltage transmission lines, defect samples of strain clamps cannot be fully and completely collected. As a result, the deep learning method based on defect sample tags cannot effectively identify all abnormalities. To solve this problem, an unsupervised anomaly detection method based on knowledge distillation is proposed, which only requires a small number of normal samples to drive the model for anomaly detection. ResNet is the framework of the teacherstudent model, and the feature activation layer after ResBlock is used for knowledge transfer. Residual‐assisted attention and pyramid‐splitting attention were used to enhance the spatial perception and multi‐scale information utilization ability of the model. This model only transmits the information of normal samples and is sensitive to abnormal samples. The proposed model outperformed the baseline by 23% and individual categories by 78% on the MVTec AD (Anomaly Detection Dataset) and outperformed the baseline by 45% and individual categories by 10% on the CIFAR10 and is also reliable for Mnist and Fashion Mnist. This method performs best (82.71%) over the existing method on the self‐built data set.

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“…YOLOv4 is a newer version of the YOLO series models proposed by Alexey Bochkovskiy et al, in 2020(Gao et al, 2021Tan et al, 2021). Compared with YOLOv3 model, YOLOv4 developed an efficient and powerful small target detection model by optimizing the trunk network, network training, activation function and other aspects combined with the previous series of models, which has a great improvement in speed and accuracy (Deng et al, 2022). The YOLOv4 network model is shown in Figure 1 (Ramachandran et al, 2017;Li et al, 2021b;Wang et al, 2022a;Sun and Xin, 2022;Xing and Chen, 2022).…”

Section: Yolov4 Network Model Based On Transfer Learning 21 Yolov4 Ne...mentioning

confidence: 99%

State identification of transfer learning based Yolov4 network for isolation switches used in substations

et al. 2022

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With the steady progress of the intelligent development of power systems, as well as the higher demand for power supply reliability. It is essential to achieve the effective monitoring of substations 24 h a day. The vigorous development of deep learning network brings strong theoretical and technical support to the unmanned and intelligent construction of the substation. To identify the on/off state of the isolation switch in the substation robot inspection image, this paper proposes a method for identifying the isolation switch state of YOLOv4 (You Only Look Once V4) network based on transfer learning. Firstly, for the insufficient number of samples, transfer learning is introduced, and the network feature extraction layer is pre-trained by using public data sets. Secondly, images of isolation switch are obtained by a fixed camera and inspection robot in the substation, and data set of isolation switch is constructed. Finally, the isolation switch data set is used to train the YOLOv4 network. The test results show that compared with YOLOv3 and YOLOv4, the network can improve the identification precision of the isolation switch.

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Research on edge intelligent recognition method oriented to transmission line insulator fault detection

Cited by 48 publications

References 31 publications

DBFN: Double Branch Fusion Network for Vital Components and Defect Detection of Transmission Line

DBFN: Double Branch Fusion Network for Vital Components and Defect Detection of Transmission Line

Live line strain clamp's DR image anomaly detection based on unsupervised learning

State identification of transfer learning based Yolov4 network for isolation switches used in substations

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