Application of Blockchain Technology in North China Power Dispatching and Auxiliary Services

Guo, Jun; Cao, Zhengfeng; Guo, Lei; Liu, Xiaotian; Shi, Peiran; Li, Min

doi:10.1109/powercon53785.2021.9697756

Cited by 1 publication

(1 citation statement)

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“…With the development of the electric power industry, high‐voltage overhead transmission lines have spread broader and broader [1], and the accidents led by the poor crimping quality of strain clamps on overhead transmission lines are increasing as well, which has caused great loss [2]. Periodic maintenance is a feasible way to deal with this problem, however, most of the existing methods for detecting the strain clamps could only be executed in power‐off conditions [3].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%