Partial discharge characteristics and development of typical XLPE power cable insulation defects

Zhao, Xuefeng; Pu, Lu; Ju, Zeli; Ren, Shuangzan; Duan, Wei; Wang, Jianhui

doi:10.1109/cmd.2016.7757955

Cited by 13 publications

(3 citation statements)

References 5 publications

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“…Internal discharge happens when a defect of closed volume within a solid or liquid insulating material occurs. The electric field in the void will be higher than the surrounding insulating medium under the operating voltage because the dielectric constant of the gas in the void is lower than the insulating medium [12]. While surface discharge mainly occurs between the inner primary insulators cable and winding insulation interfaces.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiple Partial Discharge Signal Classification Using Artificial Neural Network Technique in XLPE Power Cable

Halim

MohdRazaly

Rohani

et al. 2023

ARASET

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According to partial discharge (PD) damage in the electrodes that are not entirely bridging, the presence of PD in the high voltage (HV) power cable might lead to insulation failure. PD defects can damage cross-linked polyethylene (XLPE) cables directly, which is one of the most critical electrical issues in the industry. Poor workmanship during cable jointing, aging, or exposure to the surrounding environment is the most common cause of PD in HV cable systems. As a result, the location of the PD signals that occur cannot be classified without identifying the multiple PD signals present in the cable system. In this study, the artificial neural network (ANN) based feedforward back propagation classification technique is used as a diagnostic tool thru MATLAB software in which the PD signal was approached to determine the accuracy of the location PD signal. In addition, statistical feature extraction was added to compare the accuracy of classification with the standard method. The three-point technique is also an approach used to locate PD signals in a single line 11 kV XLPE underground power cable. The results show that the statistical feature extraction had been successful classify the PD signal location with the accuracy of 80% compared to without statistical feature extraction. The distance between PD signals and the PD source affected the result of the three-point technique which proved that a lower error means a near distance between them.

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Section: Literature Reviewmentioning

confidence: 99%

Multiple Partial Discharge Signal Classification Using Artificial Neural Network Technique in XLPE Power Cable

Halim

MohdRazaly

Rohani

et al. 2023

ARASET

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show abstract

“…Al [24] presented deep architectures for pattern recognition that can distinguish different defect types: corona, surface, and internal discharge. The authors in [25] presented the investigation of the PRPD for protrusion defect and void defect. [26] has used the CNN algorithm approach to identify defects caused by poor workmanship by utilizing the PRPD pattern images.…”

Section: Prpdmentioning

confidence: 99%

A review: partial discharge in medium voltage cross-linked polyethylene XLPE cable

Abdul Bahari,

Mei,

Mohd Ariffin

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Power cables are one of the equipment that plays a vital role in the power system network, either in High Voltage (HV) or Medium Voltage (MV) network. To ensure a reliable power supply and distribution, the integrity and the condition of the cable system must be ensured. Partial Discharge (PD) diagnosis is a valuable tool to analyse and evaluate the condition of the insulation. The purpose of this paper is to review how PD activity is being utilized to assess the condition of the XLPE (cross-linked polyethylene) cables specifically in MV level where a review of existing PD test methodology that are used by researchers and engineers to diagnose the insulation condition of the cable will be presented.

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“…Therefore, it can be analyzed using a phase-resolved partial discharge (PRPD) and a phaseresolved pulse sequence (PRPS), which are analyzed using the phase, magnitude, and frequency of the partial discharge pulse. AC partial discharge characteristics of each defect have been studied sufficiently, and are currently used for insulation diagnosis of Highvoltage alternating current (HVAC) cables [28][29][30][31][32]. In the case of DC partial discharge, since there is no phase information as there is with AC voltage, the same analysis method used for AC partial discharge cannot be used.…”

Section: Introductionmentioning

confidence: 99%

A Novel Diagnosis Method for Void Defects in HVDC Mass-Impregnated PPLP Cable Based on Partial Discharge Measurement

Kim

Lee

2021

Energies

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Mass Impregnated PPLP cable, which is applied to various high-voltage direct current (HVDC) projects due to its excellent dielectric and temperature properties, has a problem wherein voids are formed inside the butt-gap due to cavitation. However, there has been no previous research into technology for void defect identification and insulation diagnosis on HVDC MI-PPLP cables. In this paper, to propose an insulation diagnosis method for void defects in HVDC MI-PPLP cable, the direct current (DC) void discharge patterns were analyzed according to the specimen temperature and the magnitude of applied voltage using the pulse sequence analysis method. In addition, to confirm the pre-symptoms of dielectric breakdown in MI-PPLP cable due to DC void discharge, partial discharge patterns were analyzed continuously until dielectric breakdown occurred. From the experimental results, DC void discharge patterns of the same shape were obtained regardless of the specimen temperature and the magnitude of applied voltage. In addition, it was confirmed that new insulation aging patterns were generated as electrical and thermal aging occurred due to the continuous DC void discharge. Therefore, it is demonstrated that identification and insulation diagnosis of void defects in HVDC MI-PPLP cable is possible through the obtained DC void discharge and insulation aging patterns.

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Partial discharge characteristics and development of typical XLPE power cable insulation defects

Cited by 13 publications

References 5 publications

Multiple Partial Discharge Signal Classification Using Artificial Neural Network Technique in XLPE Power Cable

Multiple Partial Discharge Signal Classification Using Artificial Neural Network Technique in XLPE Power Cable

A review: partial discharge in medium voltage cross-linked polyethylene XLPE cable

A Novel Diagnosis Method for Void Defects in HVDC Mass-Impregnated PPLP Cable Based on Partial Discharge Measurement

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