Multiscale Analysis of the Aging Process of Cable Insulation

Hua, Xu; Wang, Li; Yang, Shanshui

doi:10.1109/tdei.2022.3217425

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…The thermal oxygen aging reaction of matrix resin is divided into initiation process, growth process, and termination process, as shown in Figure 7b. 24,25 The initiation process is shown in (1), where molecular chain RH generates free radical R under the action of heat, and R is oxidized to generate peroxide free radical RO 2 . The growth process is shown in ( 2)-( 6), RO 2 can seize H atoms on other molecular chains, and generate new free radical R 0 ˙and hydrogen peroxide ROOH.…”

Section: Analysis and Discussionmentioning

confidence: 99%

Effect and mechanism analysis of matrix resin type on thermal aging characteristics of semi‐conductive shielding material for high voltage cable

Liu,

et al. 2024

J of Applied Polymer Sci

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The type of matrix resin of the semi‐conductive shielding layer directly affects the thermal aging characteristics of the semi‐conductive shielding layer and the high‐voltage cable. In this paper, ethylene vinyl acetate (EVA), ethylene ethyl acrylate (EEA), and ethylene butyl acrylate (EBA) resins were used as matrix to prepare carbon black (CB) + EVA, CB + EEA, and CB + EBA shielding materials. The law of physicochemical, electrical, and mechanical properties of different matrix resin shielding materials with aging time was studied, and the influence mechanism of matrix resin on the aging characteristics of shielding materials was analyzed. The results show: with the increase of aging time, the crystallization area and the number of functional groups of the three shielding materials decreased to varying degrees. The number of functional groups in CB + EBA shielding materials decreased evenly with aging time, but that of CB + EVA and CB + EEA shielding materials changed significantly after 7 days of aging. After 60 days of aging, the crystallization area of CB + EBA shielding material changed slightly, but that of CB + EVA and CB + EEA shielding material decrease significantly. The electrical properties of the three shielding materials showed different decreasing trend with aging time. When the aging time is 7 days, the positive temperature coefficient (PTC) effect of CB + EEA shielding material decreases obviously. When the aging time is 30 days, the resistivity of CB + EVA and CB + EEA shielding material increases slowly (9 Ω cm–12 Ω cm) with the increase of temperature. When the aging time is 60 days, the resistivity of CB + EBA shielding material decreases obviously, and the PTC effect weakens obviously. Taking the mechanical properties of the shielding material as reference, the rapid deterioration stage of the mechanical properties of the three shielding materials is different. The CB + EVA and CB + EEA shielding material rapid deterioration time is 0–7 days, and the tensile strength and elongation of the shielding material are greatly reduced. The rapid deterioration stage of CB + EBA shielding material is 7–30 days, and the tensile strength and elongation decrease from 24.38 MPa and 499.5% to 14 MPa and 155.7%, respectively. This work can provide data support for the selection of matrix resin of shielding material and the fault analysis of shielding layer of high voltage cable.

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Section: Analysis and Discussionmentioning

confidence: 99%

Effect and mechanism analysis of matrix resin type on thermal aging characteristics of semi‐conductive shielding material for high voltage cable

Liu,

et al. 2024

J of Applied Polymer Sci

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A cable insulation defect classification method based on CNN-transformer

Zhao,

Duan,

et al. 2024

Front. Phys.

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Cable insulation defect detection ensures electrical safety, prevents accidents, extends equipment life and guarantees stable system operation. For the traditional cable insulation defect detection and identification of difficult problems, this paper proposes the use of ultrasonic cable insulation defect detection and combined with the Convolutional Neural Network (CNN)-transformer model of cable insulation defect classification method. Firstly, the ultrasonic probe is used to obtain different cable insulation defect signals, and then the CNN-transformer model is used to classify different cable insulation defects. The CNN is used to initially extract the characteristics of the cable insulation defects from the input signals, and then the multi-attention mechanism in the time series Transformer is used to extract the transient local and periodic global characteristics of the cable insulation defect signals. The deeper transient local features and periodic global features of the cable insulation defect signal are extracted by the multi-attention mechanism in the time series Transformer; finally, the recognition results are outputted by the fully connected layer and softmax classifier. The results show that ultrasonic reflection and transmission phenomena occur at the defects, and different defects can be accurately reflected by the defect echo time and amplitude, and the accuracy of cable insulation defect recognition using the CNN-transformer model reaches 100%, with good generalization ability.

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Research on the identification method of cable insulation defects based on Markov transition fields and transformer networks

Zhao,

Fang,

Wang

et al. 2024

Front. Phys.

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Identifying cable insulation defects is crucial for preventing system failures and ensuring the reliability of electrical infrastructure. This paper introduces a novel method leveraging the Markov transition field (MTF) and Transformer network to improve the precision of cable insulation defect identification and enhance the algorithm's noise resistance. Firstly, the algorithm performs modal transformation on the time series data acquired by the ultrasonic probe through MTF, generating corresponding images. Following this, the image data are input into a pre-trained Transformer network to achieve automated feature extraction. Subsequently, a multi-head attention mechanism is introduced, which assigns weights to the features extracted by the Transformer network, thereby emphasizing the most critical information for the identification task. Finally, more accurate defect identification is achieved based on the weighted features. The results demonstrate that this method achieves higher accuracy and stronger noise resistance compared to traditional image processing and recognition methods, making it a robust solution for cable insulation defect identification.

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Multiscale Analysis of the Aging Process of Cable Insulation

Cited by 3 publications

References 35 publications

Effect and mechanism analysis of matrix resin type on thermal aging characteristics of semi‐conductive shielding material for high voltage cable

Effect and mechanism analysis of matrix resin type on thermal aging characteristics of semi‐conductive shielding material for high voltage cable

A cable insulation defect classification method based on CNN-transformer

Research on the identification method of cable insulation defects based on Markov transition fields and transformer networks

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