| Yazarlar (3) |
Dr. Öğr. Üyesi Merve DEMİRCİ
Kafkas Üniversitesi, Türkiye |
Haluk Gözde
Türkiye |
|
Müslüm Cengiz Taplamacıoğlu
Gazi Üniversitesi, Türkiye |
| Özet |
| Power transformers are one of the most important and costly equipment for the reliability and continuity of electrical power systems. For this reason, continuous monitoring of power transformers during normal operating conditions of the power grid and early fault diagnosis from existing parameters before a fault occurs is an important task. One of the most common analysis is the Dissolved Gas Analysis (DGA) method. In the DGA analysis, the concentrations of gases formed in the transformer insulating fluid are measured, classified and used to predict failures. It is observed that the classification and diagnosis are performed by classical and artificial intelligence-based methods in the relevant literature and applications. In this study, gas data classified by machine learning method is combined with sensor fusion methods to increase the diagnosis accuracy. It has been determined that the Sequential Kalman filter, which … |
| Anahtar Kelimeler |
| Dissolved gas analysis | Fault diagnosis | Kalman filters | Machine learning | Power transformers | Sensor fusion |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | International Journal of Electrical Power & Energy Systems |
| Dergi ISSN | 0142-0615 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | İngilizce |
| Basım Tarihi | 07-2023 |
| Cilt No | 149 |
| Doi Numarası | 10.1016/j.ijepes.2023.109038 |
| Makale Linki | http://dx.doi.org/10.1016/j.ijepes.2023.109038 |
| Atıf Sayıları | |
| WoS | 39 |
| Google Scholar | 67 |
