Comparison of Partial Discharge Characteristics in SF<sub>6</sub>Gas Under AC and DC

Jo, Hyang-Eun; Wang, Guo‐Ming; Kim, Sun–Jae; Kil, Gyung-Suk

doi:10.4313/teem.2015.16.6.323

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…Due to the specific frequency spectrum of each arc fault, the TEV signals were further used to classify the type of fault using the time-frequency (TF) map. Figure 8 shows the TF spectrograms of TEV signals that indicate the distribution of signal energy along with time and frequency [19,20].…”

Section: Results and Analysismentioning

confidence: 99%

Detection and analysis of series arc using non-conventional methods in low-voltage switchboards

Wang

Kim

et al. 2018

Journal of Electrical Engineering

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Detection and analysis of series arc in low-voltage switchboards have significant meaning for preventing the electrical fires. However, the conventional current and voltage methods have low a sensitivity to sense the minute arc discharge, leading to the fail operation of arc fault circuit interrupter. Therefore, this paper dealt with the application of non-conventional methods, including the ultra-violet (UV), acoustic emission (AE), and transient earth voltage (TEV) sensor in arc detection, for the purpose of improving the detection sensitivity and reducing the potential electric fires. Three types of typical arc faults in low-voltage switchboards were simulated and the actual detection environment was configured. From the results, the wavelength of UV light emitted from arc was 200–400 nm and the arc-induced AE signal had a frequency range of 40–600 kHz. The TEV signals generated from three types of arc faults presented different frequency spectrums, based on which the time-frequency map was used to classify the fault type.

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Section: Results and Analysismentioning

confidence: 99%

Detection and analysis of series arc using non-conventional methods in low-voltage switchboards

Wang

Kim

et al. 2018

Journal of Electrical Engineering

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“…These inevitable insulation defects can be caused by metal protrusion defect, loose-contact defect and impurity defect during the preparation and long term running process. [12][13][14] Currently, various current detection methods: pulse current, ultra-high-frequency electromagnetic wave detection (UHF), transient enclosure voltage (TEV), optoacoustic detection and chemical gas sensor detection methods have been performed to detect the partial discharge according to the physical signals produced along with the partial discharge. [15][16][17][18][19] However, pulse current method is only fit for offline detection, and UHF and TEV method are easily affected by ambient interference signal.…”

Section: -2mentioning

confidence: 99%

Study on the characteristic decomposition components of air-insulated switchgear cabinet under partial discharge

Gui

Zhang

et al. 2016

AIP Advances

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Air-insulated switchgear cabinet plays a critical role in entire power transmission and distribution system. Its stability directly affects the operational reliability of the power system. And the on-line gas detection method, which evaluates the insulation status of insulation equipment by detecting the decomposition components of filled air in cabinet, becomes an innovative way to ensure the running stability of air-insulated switchgear cabinet. In order to study the characteristic gas types and production regularity of decomposition components under partial discharge, three insulation defects: needle-plate, air-gap and impurity defect are proposed to simulate the insulation defects under partial discharge in air-insulated switchgear cabinet. Firstly, the generation pathways and mechanism of composition components are discussed. Then CO and NO2 are selected as the characteristic decomposition components to characterize the partial discharge due to their high concentration and chemical stability. Based on the different change regularity of CO and NO2 concentration under different insulation defect, it provides an effective way to evaluate and predict the insulation defect type and severity in the field.

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“…As a piece of control and protection equipment in power system [1,2], gas insulated switchgear (GIS) plays a significant role in high-voltage power grids. Discovering potential defects and hidden danger in the process of operation of GIS equipment in time can ensure the reliability and security of power grid operations.…”

Section: Introductionmentioning

confidence: 99%

Frequency Feature Learning from Vibration Information of GIS for Mechanical Fault Detection

Yuan

et al. 2019

Sensors

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The reliability of gas insulated switchgear (GIS) is very important for the safe operation of power systems. However, the research on potential faults of GIS is mainly focused on partial discharge, and the research on the intelligent detection technology of the mechanical state of GIS is very scarce. Based on the abnormal vibration signals generated by a GIS fault, a fault diagnosis method consisting of a frequency feature extraction method based on coherent function (CF) and a multi-layer classifier was developed in this paper. First, the Fourier transform was used to analyze the differences and consistency in the frequency spectrum of signals. Secondly, the frequency domain commonalities of the vibration signals were extracted by using CF, and the vibration characteristics were screened twice by using the correlation threshold and frequency threshold to further select the vibration features for diagnosis. Then, a multi-layer classifier composed of two one-class support vector machines (OCSVMs) and one support vector machine (SVM) was designed to classify the faults of GIS. Finally, the feasibility of the feature extraction method was verified by experiments, and compared with other classification methods, the stability and reliability of the proposed classifier were verified, which indicates that the fault diagnosis method promotes the development of an intelligent detection technology of the mechanical state in GIS.

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Comparison of Partial Discharge Characteristics in SF₆Gas Under AC and DC

Cited by 11 publications

References 9 publications

Detection and analysis of series arc using non-conventional methods in low-voltage switchboards

Detection and analysis of series arc using non-conventional methods in low-voltage switchboards

Study on the characteristic decomposition components of air-insulated switchgear cabinet under partial discharge

Frequency Feature Learning from Vibration Information of GIS for Mechanical Fault Detection

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Comparison of Partial Discharge Characteristics in SF6Gas Under AC and DC

Cited by 11 publications

References 9 publications

Detection and analysis of series arc using non-conventional methods in low-voltage switchboards

Detection and analysis of series arc using non-conventional methods in low-voltage switchboards

Study on the characteristic decomposition components of air-insulated switchgear cabinet under partial discharge

Frequency Feature Learning from Vibration Information of GIS for Mechanical Fault Detection

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Comparison of Partial Discharge Characteristics in SF₆Gas Under AC and DC