Measurement and Diagnosis of PD Characteristics of Industrial Cable Terminations in Extreme Cold Environment

Zhou, Lijun; Bai, Longlei; Zhang, Jingkang; Cao, Weidong; Xiang, Enxin

doi:10.1109/tim.2020.3022157

Cited by 24 publications

(8 citation statements)

References 29 publications

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“…Compared with AC, PD under DC awaits to be further explored. PD for different cable insulation materials under extreme conditions need to be studied further accompanying its application, e.g., superconducting cable, PD of cable in extreme cold environments, PD of cable in vacuum pressure [159,160].…”

Section: Challenge and Discussionmentioning

confidence: 99%

Review on Detection and Analysis of Partial Discharge along Power Cables

Zhang

Pang

Liu³

et al. 2021

Energies

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Partial discharge (PD) detection and analysis plays a crucial role for acceptance testing and condition monitoring of power cables. Various aspects are related to PD in power cables from theory to practice. This paper first summarizes the PD mechanism and models used for PD analysis in power cables. Afterwards, PD detection is addressed in the aspects of off-line test, on-line test, and sensors. PD analysis is discussed in detail. Specifically, related quantities and algorithms for PD analysis are outlined. PD characteristics with affecting factors, e.g., dielectric type, load, and applied voltage are discussed. Experience on PD development trend with measurements in field is analyzed. Based on the comprehensive review, challenges of PD detection and analysis along a power cable are proposed.

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Section: Challenge and Discussionmentioning

confidence: 99%

Review on Detection and Analysis of Partial Discharge along Power Cables

Zhang

Pang

Liu³

et al. 2021

Energies

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“…In [8] a method based on convolutional neural network (CNN) has been proposed for classifying PD defects resulted from poor workmanship inside 36 kV cable terminations. In [9], after studying the characteristics of internal PDs inside cable terminations at very low temperature environments, a method for assessment of insulation status in cable terminations at such conditions has been proposed. The authors in [10] have studied the time-evolution of PD parameters for typical installation defects in MV cable terminations.…”

Section: Literature Reviewmentioning

confidence: 99%

Analytical investigations on discrimination of partial discharge defective cable termination among healthy ones with joint sheath‐ground connection

Babaee

Shahinfar

Shahrtash

2022

IET Generation Trans & Dist

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Industrial complexes suffer from crosstalk of Partial Discharge (PD) defects in cable terminations connected to the same medium voltage busbars. This phenomenon happens because the sheath-ground connections of all cables of one switchboard are first fastened together and then connected to ground, which leads to PD pulses being induced from the source termination to others. In this paper, based on mathematical investigations on PD pulse propagation and by introducing an energy-based index, a simple rule-based procedure is proposed for discrimination between defective terminations and healthy ones connected to common busbars. The proposed procedure has been verified by simulations even when there exists noise pollution or multiple defects of different strengths.

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“…PD is one of the important manifestations of GIS insulation defects, and its appearance will further aggravate equipment ageing [26]. We select four types of typical defects: free metal particles (0 type defect), metal tips (1 type defect), floating electrodes (2 type defect) and insulator air gaps (3 type defect) for GIS insulation defect diagnosis.…”

Section: Gis Insulation Defect Simulation Datamentioning

confidence: 99%

A novel adversarial transfer learning in deep convolutional neural network for intelligent diagnosis of gas‐insulated switchgear insulation defect

Wang

Yan

Jing

et al. 2021

IET Generation Trans & Dist

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Recently, numerous data-driven fault diagnosis methods have been developed, and the tasks involving the same distribution of training and test data have been well solved. However, considering the particularity of gas-insulated switchgear (GIS), collecting massive data, especially with the same distribution, is difficult. Therefore, existing fault diagnosis methods hardly achieve satisfactory insulation defect diagnosis with small datasets. Aiming at solving this problem, a novel domain adversarial transfer convolutional neural network (DATCNN) is proposed, realising the diagnosis of GIS insulation defects on small samples. First, a residual CNN is built to learn feature representations from the source and target domains. Second, the domain adversarial training strategy is used for feature transfer, where a conditional adversarial mechanism is introduced, and the joint distribution of features and labels is improved to a random linear combination, which realises the simultaneous adaptation of features and labels. Finally, the Nesterov accelerated gradient descent optimisation algorithm is used to speed up the gradient convergence. DATCNN has 99.15% and ≥89.5% diagnosis accuracy for GIS insulation defects in the laboratory and on-site, respectively. Comprehensive experiment results show the effectiveness and superiority of the proposed method in diagnosing GIS insulation defects with small samples.

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Measurement and Diagnosis of PD Characteristics of Industrial Cable Terminations in Extreme Cold Environment

Cited by 24 publications

References 29 publications

Review on Detection and Analysis of Partial Discharge along Power Cables

Review on Detection and Analysis of Partial Discharge along Power Cables

Analytical investigations on discrimination of partial discharge defective cable termination among healthy ones with joint sheath‐ground connection

A novel adversarial transfer learning in deep convolutional neural network for intelligent diagnosis of gas‐insulated switchgear insulation defect

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