TY - GEN
T1 - Deep Learning Neural Networks Diagnosis of Power Transformer through Its DGA Data
AU - Matthew, Hansel
AU - Ayu, Aqila Dzikra
AU - Suherman, Iman Herlambang
AU - Subiantoro, Aries
AU - Kusumoputro, Benyamin
N1 - Funding Information:
This research is supported with the 2020 PITTA Research Grants by Universitas Indonesia and the authors would like to express gratitude and appreciation.
Publisher Copyright:
© 2022 Institute of Advanced Engineering and Science (IAES).
PY - 2022
Y1 - 2022
N2 - Dissolved gas analysis (DGA) is the most common method used to diagnose faults in thermal power plant transformers by monitoring the concentration of various gas in transformer oil and using it to interpret the type of fault. In this study, neural networks using SGD and Adam optimizers are proposed to classify and predict electrical faults based on DGA analysis data due to the complicated interpretation of the data. Those data are obtained from IEC TC 10 databases combined with the data from a Jakarta Government's Steam Power Plant. The network designed produced a reliable fault detector for power transformer condition diagnosis.
AB - Dissolved gas analysis (DGA) is the most common method used to diagnose faults in thermal power plant transformers by monitoring the concentration of various gas in transformer oil and using it to interpret the type of fault. In this study, neural networks using SGD and Adam optimizers are proposed to classify and predict electrical faults based on DGA analysis data due to the complicated interpretation of the data. Those data are obtained from IEC TC 10 databases combined with the data from a Jakarta Government's Steam Power Plant. The network designed produced a reliable fault detector for power transformer condition diagnosis.
KW - fault detection
KW - neural network
KW - power transformers
UR - http://www.scopus.com/inward/record.url?scp=85142732216&partnerID=8YFLogxK
U2 - 10.23919/EECSI56542.2022.9946518
DO - 10.23919/EECSI56542.2022.9946518
M3 - Conference contribution
AN - SCOPUS:85142732216
T3 - International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
SP - 292
EP - 296
BT - Proceedings - 9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
A2 - Facta, Mochammad
A2 - Syafrullah, Mohammad
A2 - Riyadi, Munawar Agus
A2 - Subroto, Imam Much Ibnu
A2 - Irawan, Irawan
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
Y2 - 6 October 2022 through 7 October 2022
ER -