Fault Analysis with Data Pre-Processing Methods in Power Transformers
Yazarlar (4)
Dr. Öğr. Üyesi Merve DEMİRCİ Kafkas Üniversitesi, Türkiye
Mustafa Saka İskenderun Teknik Üniversitesi, Türkiye
Haluk Gözde
Bilim, Sanayi ve Teknoloji Bakanlığı, Türkiye
Bilim, Sanayi ve Teknoloji Bakanlığı, Türkiye
Prof. Dr. Müslüm Cengiz Taplamacıoğlu Gazi Üniversitesi, Türkiye
| Makale Türü |
Diğer (Teknik, not, yorum, vaka takdimi, editöre mektup, özet, kitap krıtiği,
araştırma notu, bilirkişi raporu ve benzeri)
(Diğer hakemli uluslararası dergilerde yayınlanan teknik not,
editöre mektup, tartışma, vaka takdimi ve özet türünden makale)
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| Dergi Adı | Turkish Journal of Electrical Power and Energy Systems | ||
| Dergi ISSN | 2791-6049 | ||
| Dergi Tarandığı Indeksler | TR DİZİN | ||
| Makale Dili | İngilizce | Basım Tarihi | 10-2023 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 19-10-2023 |
| Cilt / Sayı / Sayfa | 3 / 3 / 133–139 | DOI | 10.5152/tepes.2023.23020 |
| Makale Linki | https://tepesjournal.org/en/fault-analysis-with-data-pre-processing-methods-in-power-transformers-1355 | ||
| UAK Araştırma Alanları |
Elektrik Enerjisi ve Güç Sistemleri
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| Özet |
| Power transformers are one of the most important and costly equipment of electrical networks. Possible malfunctions that may occur in a power transformer may cause power outages as well as large energy losses. Therefore, it is important to detect the fault in transformers in advance. One of the commonly used methods for fault diagnosis in transformers is to make analyses based on gas concentrations that occur at the time of failure. This method is called dissolved gas analysis (DGA) which is based on measuring gas formation in the transformer insulating fluid during or before the fault. In this study, gas data obtained from DGA was used as the inputs of the chosen machine learning algorithms, and their diagnostic performances were measured. First, the International Electrotechnical Commission Technical Committee (IEC TC)-10 data set which is very popular in the literature was used, and then the real data set obtained from the Turkish Electricity System was applied. Since the data set consists of different sizes, it greatly affects the performance of classification algorithms. Different data preprocessing methods were applied to increase the performance of the algorithms, and how they affect the performance of the algorithms was examined. |
| Anahtar Kelimeler |
| Fault diagnosis | power transformer | preprocessing methods |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 3 |