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Determination of Type of Partial Discharge in Cubicle-Type Gas Insulated Switchgear (C-GIS) using Artificial Neural Network

Sukma T.R.a,b, Khayam U.b, Suwarnob, Sugawara R.c, Yoshikawa H.c, Kozako M.c, Hikita M.c, Eda O.d, Otsuka M.d, Kaneko H.d, Shiina Y.d

a PT PLN (Persero), Jakarta, Indonesia
b School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia
c Department of Electrical and Electronic Engineering, Kyushu Institute of Technology, Japan
d Tokyo Densetsu Service Co. Ltd., Japan

[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 IEEE.Partial discharge (PD) measurement is performed as one of diagnostic tool for condition monitoring in cubicle-type gas insulated switchgear (C-GIS). This paper presents artificial neural network (ANN) to identify the type of PD occurring in C-GIS. PD measurement results obtained from C-GIS in a field were fed to the ANN as input parameters to determine the type of PD or external noises. The C-GIS consists of two compartments of power cables and two compartments of transformers. PD measurements were conducted in the C-GIS using a commercial PD measurement system consisting of an oscilloscope and three different kinds of sensors (transient earth voltage sensor, surface current sensor and high frequency current transformer) to generate input signal waveform parameters for ANN (field data). An attempt was made to locate the PD source using the output of the sensors set at different sites. PD measurements using four kinds of artificial PD sources were also conducted in laboratory using the same PD measurement system to obtain another signal waveform parameters (laboratory data) which were used for training the ANN. Thereafter, an attempt was also made to identify PD source occurring in C-GIS using field data as input for the trained ANN. As a result, the developed ANN was found to predict the kind of PD source as void-discharge with 99% probability.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Cubicle type gas insulated switchgear,Diagnostic tools,High frequency current transformers,Input parameter,Laboratory datum,Partial discharge measurements,Surface current,Waveform parameters[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]artificial neural network,C-GIS,partial discharge,waveform parameters[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/CMD.2018.8535657[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]