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Classification of Partial Discharge Sources using Waveform Parameters and Phase-Resolved Partial Discharge Pattern as Input for the 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) is one of electrical phenomena which might occur in high voltage (HV) equipment and can be used for diagnosing the condition of the equipment. Artificial neural network (ANN) is then utilized to classify PD source in HV equipment. PD measurements were conducted to generate waveform parameters in laboratory using four kinds of artificial PD sources, three kinds of noise sources by three kinds of sensors (transient earth voltage (TEV) sensor, surface current sensor (SCS) and high frequency current transformer (HFCT)). Nine waveform parameters from one PD event were used for training and testing the ANN (ANN- WP). For further comparison, phase-resolved partial discharge (PRPD) pattern was also generated and used as input data for training and testing the other ANN (ANN-PR). Results reveal that ANN- WP provides >96% of recognition rate while ANN-PR gives >90% of recognition rate. Furthermore, the ANNs are then tested using new different artificial void defect. The results show that the ANN- WP predicted new PD data as void defect with 92 % probability while the ANN-PR prediction probability was found 96%. These results indicate that the waveform parameters can be used as an input data for ANN as well as PRPD pattern to provide sufficient accuracy for identifying the PD source. The results suggest a possibility that developed ANNs can be used as a decision-support tool in HV equipment diagnosis by comparing PD data obtained in the field.[/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]Decision support tools,High frequency current transformers,High-voltage equipments,Partial discharge sources,Phase resolved partial discharge patterns,Prediction probabilities,PRPD patterns,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,partial discharge,PRPD pattern,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.2018.8535675[/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]