Transmission line insulator fault detection based on ultrasonic technology

Zheng, Yubin; Yu, Xin; Yao, Jianchun; Wei, Sui; Yu, Xiaochen

doi:10.1088/1742-6596/1187/2/022056

Cited by 6 publications

(7 citation statements)

References 1 publication

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“…These methods have great requirements for weather conditions and are timeconsuming. In recent years, researchers have introduced the ultrasonic wave method (Yao, Yu et al, 2019) and the ultraviolet pulse method (Gao, Lyu et al, 2019). Yao, Yu et al (2019) proposed an ultrasonic wave method for detection by receiving and processing ultrasonic signals between 20-40 kHz and 80-140 kHz because ultrasound at these frequencies is emitted from the insulator failure location.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, researchers have introduced the ultrasonic wave method (Yao, Yu et al, 2019) and the ultraviolet pulse method (Gao, Lyu et al, 2019). Yao, Yu et al (2019) proposed an ultrasonic wave method for detection by receiving and processing ultrasonic signals between 20-40 kHz and 80-140 kHz because ultrasound at these frequencies is emitted from the insulator failure location. Gao, Lyu et al (2019) introduced the phase relationship between pulse waveform of an ultraviolet photoelectric sensor and pulse current waveform of partial discharge, the relationship between ultraviolet pulse frequency and apparent discharge quantity, and the attenuation characteristics of ultraviolet pulse frequency with the distance from the sensor to detection target, thus, the partial discharge of electrical equipment can be accurately detected.…”

Section: Introductionmentioning

confidence: 99%

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High Accuracy Real-Time Insulator String Defect Detection Method Based on Improved YOLOv5

et al. 2022

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Insulator string is a special insulation component which plays an important role in overhead transmission lines. However, working outdoors for a long time, insulators often have defects because of various environmental and weather conditions, which affect the normal operation of transmission lines and even cause huge economic losses. Therefore, insulator defect recognition is a crucial issue. Traditional insulator defect identification relies on manual work, which is time-consuming and inefficient. Therefore, the use of artificial intelligence to detect the defect location and recognize its class has become a key research field. By improving the classical YOLOv5 (you only look once) model, this article proposes a new method to enable high accuracy and real-time detection. Our method has three advantages: 1) Efficient-IoU (EIoU) replaces intersection over union (IoU) to calculate the loss of box regression, which overcomes that the detection is sensitive to various scale insulators in aerial images. 2) Since YOLOv5 itself detects some natural scenes in the real world, some anchors setting by default are not suitable for defect detection, this article introduces Assumption-free K-MC2 (AFK-MC2) algorithm into YOLOv5 to modify the K-means algorithm to improve accuracy and speed. 3) The cluster non-maximum suppression (Cluster-NMS) algorithm is introduced to avoid missing detection of insulators because of mutual occlusion in images and improve the computation speed at the same time. The experiments’ results show that this model can improve detection accuracy compared with YOLOv5 and realize real-time detection.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High Accuracy Real-Time Insulator String Defect Detection Method Based on Improved YOLOv5

et al. 2022

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“…The current defect detection methods can be divided into three categories including physical methods [3,4], traditional vision-based methods [5][6][7][8][9][10][11], deep learning based methods [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…The physical methods mainly include the ultrasonic wave method [3] and the ultraviolet pulse method [4] based on manual operation. In [3], the authors proposed an ultrasonic wave method for detection by receiving and processing ultrasonic signals between 20-40 kHz and 80-140 kHz because ultrasound at these frequencies mostly be emitted from the insulator failure location. In [4], the authors introduced an ultraviolet pulse method which identified the defect location by detecting the discharge of defective insulators.…”

Section: Introductionmentioning

confidence: 99%

“…laborious; easy to be affected by meteorological conditions, surrounding heat source, and other factors. [3,4] traditional vision-based methods high efficiency, low computational burden easy to be affected by the complex background; depending on accumulated experience to extract image features [5][6][7][8][9][10][11] deep learning-based methods high accuracy and feature extraction ability depending on large amounts of data. [12][13][14][15][16][17][18][19][20] Among these three categories, the physical methods are too labor work extensive, which makes it not practical for large-scale applications.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators

Wen

Luo

Chen

et al. 2021

Sensors

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By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.

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DRI-Net: a model for insulator defect detection on transmission lines in rainy backgrounds

Ji,

Gao,

Zhou

et al. 2024

J Real-Time Image Proc

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Transmission line insulator fault detection based on ultrasonic technology

Cited by 6 publications

References 1 publication

High Accuracy Real-Time Insulator String Defect Detection Method Based on Improved YOLOv5

High Accuracy Real-Time Insulator String Defect Detection Method Based on Improved YOLOv5

Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators

DRI-Net: a model for insulator defect detection on transmission lines in rainy backgrounds

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