Elektrik İletim Hatlarında Arıza Tespiti: Topluluk Makine Öğrenmesi ve Evrişimsel Sinir Ağı Yöntemlerinin Karşılaştırmalı Analizi

Yıl 2026, Cilt: 5 Sayı: 1, 150 - 168, 28.02.2026

Emrah Aslan , Yıldırım Özüpak

https://doi.org/10.62520/fujece.1581543

https://izlik.org/JA25RT76RD

Öz

Elektrik güç sistemlerinde artan yük talebini karşılamak amacıyla iletim hatlarının sayısı artarken, buna paralel olarak arıza sayıları da çoğalmaktadır. Dış çevresel etkenlerden kaynaklanan arızalar bu hatlar için ciddi tehdit oluşturabilir ve sistemin zarar görmesine neden olabilir. Bu nedenle, iletim hatlarında oluşan arızaların hızlı ve doğru bir şekilde tespit edilmesi hayati önem taşır. Bu çalışmada, iletim hatlarındaki arızaları belirlemek amacıyla yapay sinir ağı tabanlı bir model geliştirilmiştir. Öncelikle, arıza tespiti için Bagging, AdaBoost ve Gradient Boosting Sınıflandırıcı gibi çeşitli makine öğrenme algoritmaları kullanılmış ve tüm modeller eğitim ve test süreçlerinden geçirilmiştir. Test sonuçlarına göre, Gradient Boosting Sınıflandırıcı algoritması en yüksek başarıyı göstermiştir. Ancak, daha yüksek doğruluğa ulaşmak amacıyla, çalışma kapsamında derin öğrenme tabanlı bir model olan Evrişimsel Sinir Ağı önerilmiştir. Önerilen model ile %99,73 doğruluk oranı elde edilerek makine öğrenme algoritmalarından daha iyi bir başarı sağlamıştır. Bu sonuçlar, yapay sinir ağı tabanlı modelin iletim hattı arızalarını etkili bir şekilde tespit ederek güç sistemlerinin güvenilirliğini ve sürekliliğini sağlamada önemli bir rol oynadığını göstermektedir.

Anahtar Kelimeler

Derin öğrenme , Arıza tespiti , İletim hattı , Makine öğrenmesi , Güç sistemleri

Etik Beyan

Hazırlanan metin için etik kurul onayına gerek yoktur. Hazırlanan metnin herhangi bir kişi veya kurumla çıkar çatışması bulunmamaktadır.

Fault Detection in Electricity Transmission Lines: A Comparative Analysis of Ensemble Machine Learning and Convolutional Neural Network Methods

https://izlik.org/JA25RT76RD

Öz

As the number of transmission lines increases to meet the increasing load demand in electric power systems, the number of faults increases in parallel. Faults caused by external environmental factors can pose a serious threat to these lines and cause damage to the system. Therefore, fast and accurate detection of faults in transmission lines is of vital importance. In this study, an artificial neural network-based model is developed to detect faults in transmission lines. Firstly, various machine learning algorithms such as Bagging, AdaBoost and Gradient Boosting Classifier are used for fault detection and all models are put through training and testing processes. According to the test results, the Gradient Boosting Classifier algorithm showed the highest success. However, in order to achieve higher accuracy, a Convolutional Neural Network (CNN), a deep learning-based model, was proposed in this study. The proposed model achieved an accuracy rate of 99.73%, which is better than that of the machine learning algorithms. These results demonstrate that the neural network-based model plays an important role in ensuring the reliability and continuity of power systems by effectively detecting transmission line faults.

Anahtar Kelimeler

Deep learning , Fault detection , Transmission line , Machine learning , Power systems

Etik Beyan

In the prepared manuscript, there is no need for ethics committee approval. The prepared manuscript has no conflicts of interest with any individual or institution.

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