Localization with Phase Resolved Partial Discharge Measured with High Frequency Current Transformer

Lai, K.X.; Affandi, Nor Fazlinda Binte Mohd; Leck, Bing Hong; Fu, Yongyi; Ang, Seng Kok

doi:10.1109/icpadm49635.2021.9493908

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…The high-frequency current method, an improvement on the pulse current method of foreign researchers, is currently the most widely applied technique in practical work [ 9 , 10 ]. This method avoids direct connection between high-frequency current sensors and high-voltage electrical equipment; its principle involves coupling cable partial discharge signals by installing a high-frequency current sensor around the grounding wire of the power cable’s copper shield [ 11 ].…”

Section: Partial Discharge Experiments Of Xlpe Cable Insulation Defec...mentioning

confidence: 99%

Partial Discharge Signal Pattern Recognition of Composite Insulation Defects in Cross-Linked Polyethylene Cables

Qin,

Zhu,

Zhu

et al. 2024

Sensors

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To investigate the pattern recognition of complex defect types in XLPE (cross-linked polyethylene) cable partial discharges and analyze the effectiveness of identifying partial discharge signal patterns, this study employs the variational mode decomposition (VMD) algorithm alongside entropy theories such as power spectrum entropy, fuzzy entropy, and permutation entropy for feature extraction from partial discharge signals of composite insulation defects. The mean power spectrum entropy (PS), mean fuzzy entropy (FU), mean permutation entropy (PE), as well as the permutation entropy values of IMF2 and IMF13 (Pe) are selected as the characteristic quantities for four categories of partial discharge signals associated with composite defects. Six hundred samples are selected from the partial discharge signals of each type of compound defect, amounting to a total of 2400 samples for the four types of compound defects combined. Each sample comprises five feature values, which are compiled into a dataset. A Snake Optimization Algorithm-optimized Support Vector Machine (SO-SVM) model is designed and trained, using the extracted features from cable partial discharge datasets as case examples for recognizing cable partial discharge signals. The identification outcomes from the SO-SVM model are then compared with those from conventional learning models. The results demonstrate that for partial discharge signals of XLPE cable composite insulation defects, the SO-SVM model yields better identification results than traditional learning models. In terms of recognition accuracy, for scratch and water ingress defects, SO-SVM improves by 14.00% over BP (Back Propagation) neural networks, by 5.66% over GA-BP (Genetic Algorithm–Back Propagation), and by 12.50% over SVM (support vector machine). For defects involving metal impurities and scratches, SO-SVM improves by 13.39% over BP, 9.34% over GA-BP, and 12.56% over SVM. For defects with metal impurities and water ingress, SO-SVM shows enhancements of 13.80% over BP, 9.47% over GA-BP, and 13.97% over SVM. Lastly, for defects combining metal impurities, water ingress, and scratches, SO-SVM registers increases of 11.90% over BP, 9.59% over GA-BP, and 12.05% over SVM.

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Section: Partial Discharge Experiments Of Xlpe Cable Insulation Defec...mentioning

confidence: 99%

Partial Discharge Signal Pattern Recognition of Composite Insulation Defects in Cross-Linked Polyethylene Cables

Qin,

Zhu,

Zhu

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, phase-resolved PD information is also often used for PD localization. The use of various machine learning algorithms has been reported [8,9]. It is useful for identifying discharge factors and for maintenance planning.…”

Section: Introductionmentioning

confidence: 99%

Improving the Estimation of Partial Discharge Direction Using a Four-Terminal Surface Current Sensor

Otake,

Murase

2023

Energies

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To improve the insulation diagnosis of a medium-voltage (MV) switchgear, we have developed a four-terminal surface current sensor and a method to estimate the direction of partial discharge occurrence. In this method, the accuracy of the direction estimation is improved by referring only to the direct wave, and the error due to noise can be suppressed by calculating the outputs of the four terminals. The experimental results demonstrate that the detection characteristics of the sensor concur well with the theoretical characteristics derived from the detection principle. Additionally, we conducted an experiment using the developed sensor and the new method to detect the surface current generated due to partial discharge occurring in a rectangular enclosure. The results demonstrate that the occurrence direction could be estimated even for a rectangular enclosure. Furthermore, the propagation attenuation of the surface current at the corners of the enclosure was large, exhibiting an attenuation 0.25 times for the worst case. Finally, it was confirmed that noise removal using a wavelet transform could reduce the direction estimation error of the discharge occurrence by approximately half.

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Analysis of Partial Discharge Propagation Along 750 kV Transformer Winding with Transmission Line Modelling

Yang,

Zhang,

Zhan

et al. 2024

Lecture Notes in Electrical Engineering

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Localization with Phase Resolved Partial Discharge Measured with High Frequency Current Transformer

Cited by 4 publications

References 5 publications

Partial Discharge Signal Pattern Recognition of Composite Insulation Defects in Cross-Linked Polyethylene Cables

Partial Discharge Signal Pattern Recognition of Composite Insulation Defects in Cross-Linked Polyethylene Cables

Improving the Estimation of Partial Discharge Direction Using a Four-Terminal Surface Current Sensor

Analysis of Partial Discharge Propagation Along 750 kV Transformer Winding with Transmission Line Modelling

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