Research on Thermal Fault Detection Technology of Power Equipment based on Infrared Image Analysis

This paper proposes a novel metal oxide arrester (MOA) fault detection technology based on a small sample infrared image. The research is carried out from the detection process and data enhancement. A lightweight MOA identification and location algorithm is designed at the edge, which can not only reduce the amount of data uploaded, but also reduce the search space of cloud algorithm. In order to improve the accuracy and generalization ability of the defect detection model under the condition of small samples, a multi-model fusion detection algorithm is proposed. Different features of the image are extracted by multiple convolutional neural networks, and then multiple classifiers are trained. Finally, the weighted voting strategy is used for fault diagnosis. In addition, the extended model of fault samples is constructed by transfer learning and deep convolutional generative adversarial networks (DCGAN) to solve the problem of unbalanced training data sets. The experimental results show that the proposed method can realize the accurate location of arrester under the condition of small samples, and after the data expansion, the recognition rate of arrester anomalies can be improved from 83% to 85%, showing high effectiveness and reliability.

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Research on Fault Detection System of Power Equipment Based on UV and Infrared Image

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/231/1/012062 ◽

2017 ◽

Vol 231 ◽

pp. 012062

Author(s):

Qiyu Lu ◽

Kun Ding

Keyword(s):

Fault Detection ◽

Detection System ◽

Infrared Image ◽

Power Equipment

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Diagnosis and Classification Decision Analysis of Overheating Defects of Substation Equipment Based on Infrared Detection Technology

Scientific Programming ◽

10.1155/2021/3356044 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Zhigang Shi ◽

Yunlong Zhao ◽

Zhanshuang Liu ◽

Yanan Zhang ◽

Le Ma

Keyword(s):

Recognition Accuracy ◽

Infrared Image ◽

Normal Operation ◽

Power Equipment ◽

Relative Temperature ◽

Thermal Failure ◽

Detection Technology ◽

Diagnosis And Classification ◽

Substation Equipment ◽

Infrared Diagnosis

Substation equipment is not only the main part of the power grid but also the essential part to ensure the development of the national economy and People's Daily life of one of the important infrastructure. How to ensure its normal operation and find the sudden failure has become a hot issue to be solved urgently. For thermal fault diagnosis needs to classify and identify different power equipment first, this paper designed an SVM infrared image classifier, which can effectively identify three types of common power equipment. The classifier extracts HOG features from the infrared images of power equipment processed by the above segmentation and combines them with SVM multiclassification to achieve the purpose of improving the recognition accuracy. The experiment uses the classifier to identify three kinds of equipment, and the results show that the comprehensive recognition accuracy of the classifier is more than 95.3%, which is better than the traditional classification method and meets the demand for classification accuracy. In this paper, the traditional method of relative temperature difference is improved by using the temperature data of the infrared image, which can automatically judge the thermal failure level of electric power equipment. Experiments show that the diagnosis system designed in this paper can classify faults and give treatment suggestions while judging whether there are thermal faults for three types of power equipment, which verifies the feasibility and effectiveness of the substation infrared diagnosis technology designed in this paper.

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MSIS: Multispectral Instance Segmentation Method for Power Equipment

Computational Intelligence and Neuroscience ◽

10.1155/2022/2864717 ◽

2022 ◽

Vol 2022 ◽

pp. 1-13

Author(s):

Jun Shu ◽

Juncheng He ◽

Ling Li

Keyword(s):

Feature Extraction ◽

Fault Detection ◽

Feature Fusion ◽

Infrared Image ◽

Current Method ◽

Power Equipment ◽

Infrared Images ◽

Low Contrast ◽

Novel Structure ◽

Instance Segmentation

Infrared image of power equipment is widely used in power equipment fault detection, and segmentation of infrared images is an important step in power equipment thermal fault detection. Nevertheless, since the overlap of the equipment, the complex background, and the low contrast of the infrared image, the current method still cannot complete the detection and segmentation of the power equipment well. To better segment the power equipment in the infrared image, in this paper, a multispectral instance segmentation (MSIS) based on SOLOv2 is designed, which is an end-to-end and single-stage network. First, we provide a novel structure of multispectral feature extraction, which can simultaneously obtain rich features in visible images and infrared images. Secondly, a module of feature fusion (MARFN) has been constructed to fully obtain fusion features. Finally, the combination of multispectral feature extraction, the module of feature fusion (MARFN), and instance segmentation (SOLOv2) realize multispectral instance segmentation of power equipment. The experimental results show that the proposed MSIS model has an excellent performance in the instance segmentation of power equipment. The MSIS based on ResNet-50 has 40.06% AP.

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