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Author: search.filters.author.Pattanadech, NorasageSubject: search.filters.subject.partial discharge (PD)

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    Partial Discharge Classification with Transformer Neural Networks
    (2024-01-01)
    Cheypoca, Thepjit
    ;
    Promphanich, Wiboon
    ;
    Thway, Aung Ye
    ;
    Hankae, Angelina Phimpissada
    ;
    Jeenmuang, Siwakorn
    This paper introduces an approach with the Transformer Neural Networks model for partial discharge patterns classification, that consists of corona discharge, internal discharge and surface discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The phase value will be encoded into the charge array and Transformer Neural Network is constructed using Positional Embedding and Transformer Encoder Layer. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed
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    The Effect of Insulating Oil Conditions in Transformer Bushing on Dielectric Response Analysis
    (2024-01-01)
    Srisub, Chanatip
    ;
    Maneerat, Noppadol
    ;
    Rojanasunan, Warisanan
    ;
    Buranaaudsawakul, Techatat
    ;
    Pannil, Pittaya
    The bushing is one of the important parts of a transformer. Once the transformer is operated; each transformer part including its bushing starts the degradation mechanism. In some cases, the dielectric strength degradation of the bushing can lead to severe consequences. Thus, the study and diagnosis of the bushing conditions are important. This study aims to investigate the characteristics of the oil-impregnated pressboard bushing under various oil conditions by filling the new mineral oil with a controlled moisture content and letting it reach an equilibrium state to examine the dielectric response of such bushing. To determine the dielectric characteristics of the investigated bushing, A Frequency Domain Spectroscopy (FDS) device was utilized. In the experiment, the dissipation factor, capacitance, and current measurement of the bushing with different water content level were measured. The bushing test object was tested with the frequency range of 1 mHz -1 kHz. The effect of moisture clearly influenced the low-frequency ranges of the FDS. The result will be discussed in the paper.
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    Partial Discharge Characteristics of MV Switchgear Using TEV and HFCT Sensors
    (2024-01-01)
    Kingkham, Sukanya
    ;
    Smerpitak, Krit
    ;
    Inwanna, Woranan
    ;
    Jeenmuang, Siwakorn
    ;
    Buranaaudsawakul, Techatat
    This paper presents partial discharge (PD) experiments for a medium voltage (MV) switchgear. Various types of defects were simulated i.e., corona discharge, surface discharge, and floating discharge inside the simulated MV switchgear. The transient earth voltage (TEV) was employed as a PD sensor to acquire the PD signals generated by the simulated PD defects. The TEV sensors were installed on the simulated metal enclosures MV switchgear to detect electromagnetic (EM) pulse radiated through such metal housing. Besides, high-frequency current transformer (HFCT) sensors were also used for a purpose similar to that of a TEV. Based on laboratory test results, it was found that when the external structural material and internal partition wall of the MV switchgear have greater insulation properties, some of the PD signals detected by the TEV sensor tend to exhibit a significant decrease in signal magnitude. The detected PD results of the simulating PD sources inside the MV switchgear will be presented in this paper. Moreover, the field test PD experiment for the MV switch gear is also reported.
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    Partial Discharge Classification With 1D Convolutional Neural Network
    (2024-01-01)
    Cheypoca, Thepjit
    ;
    Promphanich, Wiboon
    ;
    Thway, Aung Ye
    ;
    Hankae, Angelina Phimpissada
    ;
    Jeenmuang, Siwakorn
    This paper introduces a novel approach for classifying with the 1D Convolutional Neural Network model for partial discharge patterns, that consists of corona discharge, surface discharge and internal discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The Artificial Neural Network for the classification model was constructed. Moreover, 2×1D CNN feature extraction was utilized to reduce the curse of dimensionality in the dense layer of the proposed PD classification model. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed.