| Yazarlar (5) |
Hidayet Yakupoğlu
|
|
Haluk Gözde
Türkiye |
|
Müslüm Cengiz Taplamacıoğlu
Gazi Üniversitesi, Türkiye |
|
Mehmet Akif Şenol
İstanbul Topkapı Üniversitesi, Türkiye |
Dr. Öğr. Üyesi Merve DEMİRCİ
Kafkas Üniversitesi, Türkiye |
| Özet |
| Wind turbines have a rapidly increasing penetration worldwide as a clean and renewable energy source. The main factors and risks affecting the operational costs in wind farms established in large power plants are turbine failures and long downtimes. Periodic maintenance of wind turbines can boost efficiency and reliability. However, traditional routine checks make it difficult to detect wind turbine failures. Fault detection through a supervisory control and data acquisition (SCADA) system depends on the reliability of its data and the success of the model. This paper proposes a new model to classify the failures of the wind turbine condition monitoring system. In order to improve the success of the model, historical turbine measurement and failure data are pre-processed and filtered. Then, class balancing and data augmentation methods are applied with the Synthetic Minority Oversampling Technique (SMOTE). In … |
| Anahtar Kelimeler |
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum |
| Bildiri Dili | İngilizce |
| Kongre Adı | International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2024) |
| Kongre Tarihi | 04-11-2024 / 06-11-2024 |
| Basıldığı Ülke | Maldiv |
| Basıldığı Şehir |
