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Subject: search.filters.subject.Corona discharge

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    Comparison of dissolved gases in natural ester under partial discharges
    (2019-06-01)
    Jongvilaikasem, Korraya
    ;
    Maneerot, Sakda
    ;
    Jariyanurat, Kittipod
    ;
    Pattanadech, Norasage
    Currently, natural ester is alternative liquid insulation applied for both distribution and power transformers. Generally, transformers are expected to operate stably and reliably. However, their functions may fail during operation. Dissolved gases analysis is one of the worldwide accepted diagnostic techniques to examine the insulation integrity of the transformers. This paper presents the analysis of dissolved gases generated from partial discharge (PD) experiments i.e. corona discharge, surface discharge and internal discharge which were conducted in the test cell filled with natural ester (FR3). To simulate corona discharge, a needle electrode with a tip radius of 10, 20, and 40 micrometers was used as high voltage electrode and the plane electrode with a diameter of 45 mm was used as a grounded electrode. The gap distance was fixed at 32 mm. The corona discharge experiment was performed for 2 hours at the test voltage level of 35, 40, 45 kV respectively. In case of surface discharge, the pressboard samples i.e. non-impregnated pressboards, 8-hour impregnated pressboards, and 16-hour impregnated pressboards were inserted between the 20-micrometer tip radius needle and grounded plane. Then, the test voltage of 25 kV was applied to the electrode system for 1 hour. Besides, the surface discharge was tested at 35 and 45 kV respectively. For internal discharge, the pressboard samples i.e. non-impregnated pressboards, 8-hour impregnated pressboards, and 16-hour impregnated pressboards were inserted between the plane-plane electrodes. Then the test voltage of 25 kV was applied to the electrode system for 1 hour. Moreover, the internal discharge was tested also at 35 and 45 kV. Their natural ester samples were sampling from the test cell for each experiment and the dissolved gases were measured and analyzed. From the test results, it was found that the types and quantity of dissolved gases depended strongly on the type of discharge and material samples.
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    Polarity effect of pulsed corona discharge plasma on seed surface modification
    (2017-10-19)
    Bunme, Pawita
    ;
    Khamsen, Natthaporn
    ;
    Kasemsuwan, Varakorn
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Pichetjamroen, Achara
    This paper presents an experimental analysis and underlying mechanism of positive and negative pulsed corona discharge induced atmospheric non-Thermal plasma. We designed a system based on point-plane configuration to produce direct corona discharge plasma (DCP) and hybrid corona discharge plasma (HCP) systems. The polarity effect of corona discharge for surface modification application on rice seeds was investigated under atmospheric dry air ambient. The reactive species under negative and positive corona discharge plasma were also studied for optimization design. The results show that only negative pulsed corona discharge is feasible for DCP system while both positive and negative pulsed corona discharge can be employed for in HCP system.
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    Pattern classification of partial discharge in high voltage equipment by regression analysis
    (2008-10-06)
    Ludpa, S.
    ;
    Pattanadech, N.
    ;
    Leelajindakrairerk, M.
    ;
    Yutthagowith, P.
    This paper introduces a statistical classification in regression model to classify partial discharge (PD) patterns into four categories in corona: high voltage side in air, corona at low voltage side in air, surface in air, and internal discharge. There are nine independent variables from fingerprint analysis which mainly are skewness, kurtosis, asymmetry and cross correlation following φ - q - n PD patterns. The variable independent data are divided into two group: the forms for training the model and the pattern for test the model. The created algorithm investigates the best of parameter group from all of independence variables for creating the regression model. In this work, the group of five best parameters for classification are selected. The results show, that only five parameters in the model has a good performance to classify PD pattern with the classification accuracy of 100%. © 2008 IEEE.