TY - GEN
T1 - Intelligent Fault Diagnosis for Power Transformer Based on DGA Data Using Support Vector Machine (SVM)
AU - Dhini, Arian
AU - Surjandari, Isti
AU - Faqih, Akhmad
AU - Kusumoputro, Benyamin
PY - 2019/4/11
Y1 - 2019/4/11
N2 - Transformer is a crucial element in distributing electricity from power plant. Disturbance in transformer operation should be avoided. Dissolved gas analysis (DGA) has been known as one of the most effective tools to monitor the health of transformer. There are various methods in interpreting DGA manually, such as IEEE and IEC-based methods. However, those methods still require the human expertise. Fast and accurate fault diagnosis in the transformer remains a challenge. This study proposes an intelligent system to diagnose fault types in the transformer using data mining approach, i.e. support vector machine (SVM). SVM has been known for its robustness, good generalization ability and unique global optimum solutions. IEC TC10 databases are used as data to illustrate the performance of multistage support vector machine (SVM). The proposed system yields effective transformer fault diagnosis with high recognition rate, which is around 90%.
AB - Transformer is a crucial element in distributing electricity from power plant. Disturbance in transformer operation should be avoided. Dissolved gas analysis (DGA) has been known as one of the most effective tools to monitor the health of transformer. There are various methods in interpreting DGA manually, such as IEEE and IEC-based methods. However, those methods still require the human expertise. Fast and accurate fault diagnosis in the transformer remains a challenge. This study proposes an intelligent system to diagnose fault types in the transformer using data mining approach, i.e. support vector machine (SVM). SVM has been known for its robustness, good generalization ability and unique global optimum solutions. IEC TC10 databases are used as data to illustrate the performance of multistage support vector machine (SVM). The proposed system yields effective transformer fault diagnosis with high recognition rate, which is around 90%.
KW - condition monitoring
KW - Dissolved gas analysis
KW - fault diagnosis
KW - support vector machine
KW - transformer
UR - http://www.scopus.com/inward/record.url?scp=85065036023&partnerID=8YFLogxK
U2 - 10.1109/ICSRS.2018.8688719
DO - 10.1109/ICSRS.2018.8688719
M3 - Conference contribution
AN - SCOPUS:85065036023
T3 - Proceedings - 2018 3rd International Conference on System Reliability and Safety, ICSRS 2018
SP - 294
EP - 298
BT - Proceedings - 2018 3rd International Conference on System Reliability and Safety, ICSRS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on System Reliability and Safety, ICSRS 2018
Y2 - 24 November 2018 through 26 November 2018
ER -