[vc_empty_space][vc_empty_space]
Design of partial discharge location identifier software for high voltage generator using artificial neural network
Suprayogi B.a, Khayam U.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, 40132, Indonesia
[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]© 2015 IEEE.This paper deals with design of software for partial discharge (PD) pattern recognition and signal assessment on power generator using artificial neural network (ANN). The identified PD signal was the simulation of PD measurement result on high voltage generator. The simulated signal contains background noise (BGN) and phase-resolved PD pattern (PRPD pattern) data. The characteristics of information processing systems of ANN is similar to biological neural networks. ANN method is a decision making system using the learning system that works based on knowledge based database. PD pattern recognition is done by observing the phase of PD signal, while the PD location identification of the high voltage generator is adapted based on the standards and also previous studies on the phase of PRPD pattern. Database is obtained from PD data collection results of PRPD signal simulation. The performance of the software is tested in determining the PD location on the generator. The software testing is done by providing a data test of PRPD patterns and BGN. This testing results showed that the software has been proven to have a high accuracy degree for PD pattern recognition.[/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]Biological neural networks,identifier,Information processing systems,Knowledge-based database,Location identification,Partial discharge location,Partial discharge pattern,Phase resolved pd patterns[/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,identifier,knowledge based – database,partial discharge,phased resolved partial discharge pattern,software,testing[/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/ICEVTIMECE.2015.7496664[/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]