Gerilim Kaynaklı Eviricinin Faz Akımlarının İzlenmesiyle Açık Devre Arızalarının Teşhisi

Yıl 2024, Cilt: 36 Sayı: 2, 67 - 82, 30.09.2024

Serenay Çelik , Servet Tuncer

Öz

Gerilim kaynaklı eviricilerin endüstriyel uygulamalarda yaygın olarak kullanılmasıyla, meydana gelen arızaların tanımlanması önemli bir araştırma konusu haline gelmiştir. Bu çalışmada, üç-fazlı iki-seviyeli eviricideki 24 farklı tekli ve çoklu açık anahtar devre arızaları incelenmiş, arızanın bulunduğu kol ve arızalı anahtarın tespiti yapılmıştır. Matlab/Simulink ortamında benzetimi yapılan eviricinin çıkış faz akımlarının ortalama, rms (etkin) değerlerinin yanı sıra ortalama/rms oranları da kullanılarak yük bağımlılığı problemi giderilmiştir. Çalışmada, destek vektör makineleri (SVM: Support Vector Machines), K-en yakın komşular (KNN: K-Nearest Neighbours), yapay sinir ağı (ANN: Artificial Neural Network) ve uzun kısa süreli bellek (LSTM: Long Short Term Memory) gibi dört farklı sınıflandırma modeli kullanılmış olup her bir modelin başarınımı ayrı ayrı değerlendirilmiştir. Benzetim sonuçlarından, önerilen arıza teşhis ve sınıflandırma tekniklerinin tekli, çiftli ve üçlü anahtar arıza durumlarındaki tahmin başarısı yüksek doğrulukla sağlanılmıştır.

Anahtar Kelimeler

Açık devre arızası, makine öğrenmesi, derin öğrenme, faz akımları

Diagnosis of Open Circuit Faults by Monitoring Phase Currents of Voltage Source Inverter

Öz

With the widespread use of voltage source inverters in industrial applications, the identification of faults has become an important research topic. In this study, 24 different single and multiple open switch circuit faults in a three-phase two-level inverter were examined, and the branch where the fault was located and the faulty switch were identified. The load dependency problem was eliminated by using the average, rms (effective) values of the output phase currents of the inverter simulated in the Matlab/Simulink environment as well as the average/rms ratios. In the study, four different classification models such as Support Vector Machines (SVM), K-nearest Neighbors (KNN), Artificial Neural Network (ANN) and Long Short Term Memory (LSTM) were used and the performance of each model was evaluated separately. From the simulation results, the prediction success of the proposed fault diagnosis and classification techniques in single, double and triple switch fault cases was achieved with high accuracy.

Anahtar Kelimeler

Open circuit fault, machine learning, deep learning, phase currents

Kaynakça

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