State Detection of Electrical Equipment Based on Infrared Thermal Imaging Technology

Yuan, Hejin; Chen, Xiuxin; Wang, Yu; Су, Минг

doi:10.1007/978-3-030-31654-9_22

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…The collected images need to be checked and judged one by one by experienced engineers. Despite its effectiveness, the manual method is very laborious and slow [9][10][11][12][13]. To prevent major emergency accidents in substations, it is of great significance to study the segmentation of abnormal temperature regions and thermal fault diagnosis based on infrared images of electrical equipment in substations.…”

Section: Introductionmentioning

confidence: 99%

Thermal Fault Diagnosis of Electrical Equipment in Substations Based on Image Fusion

Lu¹,

Liu²,

Yuan³

2021

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Infrared thermal imaging can diagnose whether there are faults in electrical equipment during non-stop operation. However, the existing thermal fault diagnosis algorithms fail to consider an important fact: the infrared image of a single band cannot fully reflect the true temperature information of the target. As a result, these algorithms fail to achieve desired effects on target extraction from low-quality infrared images of electrical equipment. To solve the problem, this paper explores the thermal fault diagnosis of electrical equipment in substations based on image fusion. Specifically, a registration and fusion algorithm was proposed for infrared images of electrical equipment in substations; a segmentation and recognition model was established based on mask region-based convolutional neural network (R-CNN) for the said images; the steps of thermal fault diagnosis were detailed for electrical equipment in substations. The proposed model was proved effective through experiments.

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Section: Introductionmentioning

confidence: 99%

Thermal Fault Diagnosis of Electrical Equipment in Substations Based on Image Fusion

Lu¹,

Liu²,

Yuan³

2021

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Investigation of the Algorithm to Find Defects in High-voltage Insulators for an Automated Thermal Imaging Control System

Zaripova¹,

Zaripov

Usachev

2021

E3S Web Conf.

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This paper describes a software algorithm for detecting defective insulating structures using infrared images. The defect detection criteria are based on a joint analysis of the mean and standard value of the brightness distribution of a set of insulators. The effectiveness of the developed criteria is substantiated by the results of laboratory tests of a significant number of insulators removed from high-voltage lines according to the results of thermal imaging diagnostics. Simultaneous analysis of thermograms of the same type of insulating structures according to the proposed algorithm is more effective in comparison with the subjective assessment of each of them separately, which was used earlier. In addition, this approach allows to reduce the time of analysis and decision-making based on the results of diagnostics.

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An improved HOG-SVM recognition algorithm of power equipment with infrared images

Qian

Zhang

Wang

et al. 2021

J. Phys.: Conf. Ser.

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Infrared image recognition of power equipment is a necessary prerequisite and a key step to using the infrared image for equipment defect detection, while one of the difficulties is to solve the problem of low accuracy of equipment recognition, which is caused by the difficulty of extracting equipment feature quantity caused by different rotation Angle and scale of equipment image. In this paper, 12 kinds of common power equipment, such as lightning arresters, current transformers, voltage transformers, circuit breakers, and insulators, are taken as the research objects. An improved HOG-SVM algorithm for infrared image recognition of power equipment with rotation invariance is proposed. Experimental results show that the proposed method can effectively compensate for the shortcoming of the HOG algorithm without rotation invariance, and the average recognition accuracy of 12 types of equipment reaches 92.14%, which significantly improves the recognition accuracy of infrared images of power equipment, and verifies the effectiveness and practicability of the proposed algorithm.

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State Detection of Electrical Equipment Based on Infrared Thermal Imaging Technology

Cited by 4 publications

References 6 publications

Thermal Fault Diagnosis of Electrical Equipment in Substations Based on Image Fusion

Thermal Fault Diagnosis of Electrical Equipment in Substations Based on Image Fusion

Investigation of the Algorithm to Find Defects in High-voltage Insulators for an Automated Thermal Imaging Control System

An improved HOG-SVM recognition algorithm of power equipment with infrared images

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