Elektrik İletim Sistemlerinde Hata Tespiti ve Sınıflandırmösı

Yıl 2025, Cilt: 15 Sayı: 1, 470 - 487, 15.03.2025

Yusuf Sevim

https://doi.org/10.31466/kfbd.1604790

Öz

Elektrik iletim hatlarının (EİH) kısa devre arızalarını hızlı ve etkili bir şekilde tespit etmek çok önemlidir. Literatürdeki çoğu yöntem, arıza tespiti için sınıflandırma algoritmalarını kullanmaktadır, ancak bu yöntemlerin gerçek zamanlı uygulamalarda kullanımı, arıza tespit süresini artırmaktadır. Bunun nedeni, arıza tespit süreci sınıflandırma algoritması ile gerçekleştirilirken, gelen verilerin özelliklerinin sürekli olarak bir pencere fonksiyonu kullanılarak çıkarılması gerektiğidir. Bu çalışmada, arıza tespit süresini azaltmak için gerçek zamanlı arıza tespiti için uygun olan temel bileşen analizi (TBA) veya bağımsız bileşen analizi (BBA) algoritmaları önerilmektedir. Ayrıca, sınıflandırma hızını ve doğruluğunu artırmak için belirli bir zaman aralığında hesaplanan EİH sinyallerinin zaman alanı istatistiksel özellikleri önerilmektedir. Sonuçlar, TBA ve BBA algoritmalarının gerçek zamanlı veri akışlarında tüm arızaları tespit edebildiğini ve önerilen özellikler ile 10 arıza için sınıflandırma sonuçlarının %100 olduğunu göstermektedir.

Anahtar Kelimeler

Hata bulma, Özellik Çıkarımı, Bağımsız Bileşen Analizi, Temel Bileşen Analizi, S dönüşümü

Power Transmission Line Fault Detection and Classification

Öz

It is very important to find short circuit faults of power transmission lines (PTL) quickly and efficiently. Most methods in the literature use classification algorithms for fault detection, but their use in real-time applications increases fault detection time. The reason for this that while the fault detection process is performed with the classification algorithm, the features of incoming data must be extracted continuously by using a window function. In this study, principal component analysis (PCA) or independent component analysis (ICA) algorithms that are suitable for real-time fault detection are proposed to decrease the fault detection time. Besides, time-domain statistical properties of the PTL signals computed over a period of time are proposed to increase classification speed and accuracy. The results show that PCA and ICA algorithms can detect all faults in real-time data streams, and the classification results are 100% for 10 faults with the proposed features.

Anahtar Kelimeler

Fault detection, Feature Extraction, Independent Component Analysis, Principal Component Analysis, S Transform.

Kaynakça

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