Insulator Contamination Perception Based on Feature Fusion of Infrared Image and Meteorological Parameters

Wang, Hongxia; Wang, Bo; Li, Min; Luo, Peng; Ma, Hengrui; Ma, Fuqi

doi:10.3389/fenrg.2021.746378

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…This can lead to cracking and detachment of the coating [3]. Moreover, an excessively thick coating leads to material waste during the coating process, resulting in economic losses [4][5]. Therefore, it is crucial to design an intelligent coating thickness measurement device that can measure the thickness of the coating on the surface of the transmission tower without causing damage to the coating itself.…”

Section: Background and Significancementioning

confidence: 99%

A Dual Function Coating Thickness Measuring Device

Chen,

Li,

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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In engineering applications, the harsh external environment leads to the erosion of the surface of the tower. The existence of the coating layer provides better protection for the tower, but the uniformity of the thinness and thickness of the coating layer has a great impact on the performance of the material. In this paper, a dual-function tower coating thickness measurement device is designed. The device can automatically measure the thickness of the coating layer by magnetic induction or eddy current thickness measurement principle for the towers with magnetic or non-magnetic materials. The small prototype experiments show that the average error of the measurement of the non-magnetic substrate is only 0.74%, and the average error of the measurement of the magnetic substrate is only 6%, and it has the characteristics of low cost and long service life.

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Section: Background and Significancementioning

confidence: 99%

A Dual Function Coating Thickness Measuring Device

Chen,

Li,

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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show abstract

“…It should be mentioned here that mask RCNN is more suitable for this task compared to normal RCNN. This is because a normal RCNN image can segment insulator area through bounding box method [9]. In this method, the algorithm detects a closed boundary, which tightly fits the object.…”

Section: Image Pre‐processingmentioning

confidence: 99%

“…[8] is also not automated and therefore limits its field applications. Very recently, the classification of contamination severity based on a hybrid feature fusion approach by combining the IRT image along with meteorological features (humidity and temperature) has been reported [9]. Although the method used deep learning network VGGNet16 for feature extraction, yet the reported precision is only 78.75%.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network and Bi‐directional long short memory hybrid deep network aided infrared image classification framework for non‐contact monitoring of overhead insulators

Das

Deb

Chatterjee

et al. 2022

IET Signal Processing

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In this paper, a novel vision-based automated framework for estimation of pollution severity of outdoor insulators is proposed. The correct estimation of pollution severity is important to prevent the premature flashover of insulators. Existing methods to determine the degree of contamination of insulator surface requires direct contact with the insulator, which is practically problematic. Considering the aforesaid fact, in this article, a novel infrared thermal (IRT) image-based automated framework is proposed for noncontact monitoring the surface condition of outdoor insulators. A large number of IRT images corresponding to different contamination levels were captured from several porcelain disc insulators using a thermal camera. The captured IRT images were initially segmented using mask region-based convolutional neural network to remove the effect of background. Then, a hybrid deep learning network consisting of convolutional neural network and Bi-directional long short memory (CNN-BiLSTM) is designed for automated classification of IRT images. It has been observed that the proposed network has achieved an accuracy of 98.86%, specificity of 99.72%, precision of 98.86% and F-1 score of 98.86%, respectively. A comparative study with other deep learning models indicated that the proposed CNN-BiLSTM network delivered better performance. Hence, the proposed framework can be used in real-life for non-contact condition monitoring of insulators.

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“…The short circuit caused by overheating will damage the electrical equipment, stop power supply of the building, and cause economic losses. The infrared imager can sense the surface temperature of electrical equipment, which is an effective way for monitoring overheating (Gao et al, 2021;Wang et al, 2021;Chen et al, 2022). During daily patrol inspection, the busduct can be inspected with an infrared imager.…”

Section: Introductionmentioning

confidence: 99%

Overheating fault alarming for compact insulated busways in buildings by gas sensing

Xiong¹,

Li²,

Li³

et al. 2023

Front. Energy Res.

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Addressing the overheating fault detection and alarming of insulated busways in buildings, a system based on sensing volatile gases generated by the thermal degradation of the busduct insulation layer was proposed. By monitoring the concentration of volatile gases in the environment, the overheating fault of the busducts can be found early. The thermal degradation process of the busway insulating layer is analyzed, and the pyrolysis characteristic gas at low temperature is studied. The experimental platform has been built, by which the relation between the concentration of volatile gases and temperature of the insulated layer has been studied. By testing, the concentration of volatile organic compounds (VOCs) is proposed as the basis for judging the overheating fault in the alarming system. With the collected samples for training and testing, the AdaBoost classifier is used to identify the overheating fault. Finally, the design of the overheating fault alarming system is given.

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Insulator Contamination Perception Based on Feature Fusion of Infrared Image and Meteorological Parameters

Cited by 9 publications

References 31 publications

A Dual Function Coating Thickness Measuring Device

A Dual Function Coating Thickness Measuring Device

Convolutional neural network and Bi‐directional long short memory hybrid deep network aided infrared image classification framework for non‐contact monitoring of overhead insulators

Overheating fault alarming for compact insulated busways in buildings by gas sensing

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