Intelligent Fault Diagnosis for Power Transformer Based on DGA Data Using Support Vector Machine (SVM)

被引：0

作者：

Dhini, Arian ^{[1
]}

Surjandari, Isti ^{[1
]}

Faqih, Akhmad ^{[2
]}

Kusumoputro, Benyamin ^{[2
]}

机构：

[1] Univ Indonesia, Fac Engn, Dept Ind Engn, Depok, Indonesia

[2] Univ Indonesia, Fac Engn, Dept Elect Engn, Depok, Indonesia

来源：

2018 3RD INTERNATIONAL CONFERENCE ON SYSTEM RELIABILITY AND SAFETY (ICSRS) | 2018年

关键词：

dissolved gas analysis; support vector machine; transformer; condition monitoring; fault diagnosis; DISSOLVED-GAS ANALYSIS; OIL;

D O I：

10.1109/ICSRS.2018.00055

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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%

引用

页码：294 / 298

页数：5

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