Intelligent learning approach for UHF partial discharge localisation in air‐insulated substations

Zheng, Quanfu; Luo, Lingen; Song, Hui; Sheng, Gehao; Jiang, Xiuchen

doi:10.1049/hve.2019.0342

Cited by 19 publications

(9 citation statements)

References 23 publications

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“…Till date, the PD localization for the GIL has mainly been based on the detection of ultra–high frequency (UHF) signals and ultrasonic signals [7–9]. By contrast, the optical‐only PD localization method performs with high sensitivity, no electromagnetic interference and no mechanical vibration interference [10, 11], which has promising research prospects and application value.…”

Section: Introductionmentioning

confidence: 99%

Method of GIL partial discharge localization based on natural neighbour interpolation and ECOC‐MLP‐SVM using optical simulation technology

et al. 2021

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Partial discharge (PD) is one of the main causes rendering the deterioration of the insulation state in gas-insulated transmission line (GIL). Accurate and timely localization of the PD source is essential to ensure the safe and stable operation of the GIL. At present, optical PD detection technology shows advantages in terms of high sensitivity and strong anti-interference performance. Therefore, an optical PD localization method based on optical simulation fingerprint database is proposed. The introduction of simulation conquers the difficulty of obtaining a PD fingerprint database in field experiments. This method constructs a simulation fingerprint database by performing PD simulation in a GIL simulation model of the same size as the actual GIL. The natural neighbour interpolation algorithm is applied to expand the simulation fingerprint database to cover all locations in the GIL. The two-level localization method proposed is to match the PD fingerprint to be tested with the expanded fingerprint database, which can reduce the amount of calculation while maintaining the localization accuracy. The experimental results show that the average localization error of this method is only 9.7 mm, and the localization time is reduced by about 11 times compared with the normal one-level localization method. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Section: Introductionmentioning

confidence: 99%

Method of GIL partial discharge localization based on natural neighbour interpolation and ECOC‐MLP‐SVM using optical simulation technology

et al. 2021

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“…-3 50/60 Hz [16,32,34,35]. The sensitivity of these methods is impacted by the frequency and air pressure because these two parameters alter the PD pulse's characteristics as discussed above.…”

Section: Effect Of Frequency and Air Pressure On Partial Discharge Characteristicsmentioning

confidence: 99%

“…The existing methods have been designed to detect the PD pulse activity under atmospheric pressure and at a frequency of 50/60 Hz [16,32,34,35]. The sensitivity of these methods is impacted by the frequency and air pressure because these two parameters alter the PD pulse's characteristics as discussed above.…”

Section: Effect Of Frequency and Air Pressure On Partial Discharge Ch...mentioning

confidence: 99%

Partial discharge detection and identification at low air pressure in noisy environment

2021

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Increasing demand for electric power in more electric aircraft requires a higher operating voltage for the power system that leads to higher electric stress on the insulation system. Operating at high altitudes where the electrical strength of air is weakened exposes the insulation system to even higher levels of electrical stress. As such, the probability of partial discharges (PD), which result in insulation degradation and failure, is higher. The presence of switching circuits in the power distribution system of the more electric aircraft is capable of producing high levels of noise. This noise in combination with the background noise and parasitic impedances will cause high amplitude ringing in the measured partial discharge signal waveform. Here, a method based on the combination of the wavelet and energy techniques is employed to detect PD pulses in a noisy environment under the low air pressure condition. To verify the technique, a laboratory setup consisting of two separate PD sources mounted in low air pressure (33 kPa) chamber is developed where sine and square waveforms are used as the applied voltage. The obtained results demonstrate that the proposed approach offers low computational complexity, high performance in PD phase localization, and robustness in a noisy environment.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Therefore, the operation and maintenance technology, such as detection and diagnosis of XLPE insulation fault state, is an urgent research topic. Partial discharge and leakage current are two main ways to characterise the insulation performance of power equipment [7][8][9][10][11]. However, the research on the formation mechanism, characteristic law and identification technology of partial discharge and leakage current of XLPE cable under DC is still relatively insufficient, and the research on combined detection and identification of partial discharge and leakage current is relatively less.…”

Section: Introductionmentioning

confidence: 99%

Application of multi‐source information fusion based on D‐S evidence theory in insulation defect identification of DC XLPE cable

Huang

et al. 2021

High Voltage

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During the installation and laying process of cross-linked polyethylene (XLPE) cable, it is difficult to avoid the influence of external harsh environment, which leads to insulation deterioration and failure. The partial discharge and leakage current play an important role in formation mechanism, characteristic law and identification technology of DC XLPE cable defect. However, the defect identification accuracy of single source data is limited. The correlation between partial discharge and leakage current of typical defect is analysed here. Based on the shape characteristic parameters of typical DC partial discharge spectrum and the energy characteristic parameters of leakage current wavelet decomposition node, a weighted Dempster-Shafer (D-S) evidence theory is used to fuse and identify different information sources. The results show that the classification rate of the proposed method can reach 88%, which can effectively identify insulation defects.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Intelligent learning approach for UHF partial discharge localisation in air‐insulated substations

Cited by 19 publications

References 23 publications

Method of GIL partial discharge localization based on natural neighbour interpolation and ECOC‐MLP‐SVM using optical simulation technology

Method of GIL partial discharge localization based on natural neighbour interpolation and ECOC‐MLP‐SVM using optical simulation technology

Partial discharge detection and identification at low air pressure in noisy environment

Application of multi‐source information fusion based on D‐S evidence theory in insulation defect identification of DC XLPE cable

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