Classification of Power Quality Disturbances Using Convolutional Neural Networks with Wavelet Scalogram Images

Öz

Accurate and fast detection of power quality disturbances (PQDs) is of critical importance for the reliability and efficiency of electric power systems. In this study, a time-frequency based approach is proposed for the classification of power quality disturbances. In this context, signals belonging to 15 different PQD classes were generated through mathematical models, and a dataset was created using a sampling frequency of 12.8 kHz. The generated signals were converted into scalogram images using Continuous Wavelet Transform (CWT) to obtain a suitable input format for deep learning models. In the classification phase, the AlexNet, SqueezeNet, and ResNet-18 architectures were trained using a transfer learning approach. The performance of the models was evaluated under noise-free, 40 dB, and 30 dB SNR (signal to noise ratio) levels. The results showed that all models achieved high accuracy rates in the noise-free condition, but performance differences became significant with increasing noise levels. In particular, the ResNet-18 architecture was the most successful model, maintaining high classification accuracy even under low SNR conditions. The study results show that scalogram-based data generation and deep learning methods offer an effective and reliable approach for classifying power quality disturbances.

Anahtar Kelimeler

• Power quality disturbances
• Classification
• Scalogram
• Deep learning

Dalgacık Skalogram Görüntüleri Kullanılarak Evrişimli Sinir Ağları İle Güç Kalitesi Bozulmalarının Sınıflandırılması

Öz

Güç kalitesi bozulmalarının (PQD) doğru ve hızlı bir şekilde tespit edilmesi, elektrik güç sistemlerinin güvenilirliği ve verimliliği açısından ciddi bir öneme sahiptir. Bu çalışmada, güç kalitesi bozulmalarının sınıflandırılması amacıyla zaman–frekans temelli bir yaklaşım önerilmiştir. Bu kapsamda, 15 farklı PQD sınıfına ait sinyaller matematiksel modelleri aracılığıyla üretilmiş ve 12,8 kHz örnekleme frekansı kullanılarak veri kümesi oluşturulmuştur. Üretilen sinyaller, Sürekli Dalgacık Dönüşümü (CWT) yardımıyla skalogram görüntülerine dönüştürülerek derin öğrenme modelleri için uygun bir giriş formatı elde edilmiştir. Sınıflandırma aşamasında transfer öğrenme yaklaşımı kullanılarak AlexNet, SqueezeNet ve ResNet-18 mimarileri eğitilmiştir. Modellerin performansı gürültüsüz, 40 dB ve 30 dB SNR (sinyal gürültü oranı) seviyeleri altında değerlendirilmiştir. Elde edilen sonuçlar, tüm modellerin gürültüsüz durumda yüksek doğruluk oranlarına ulaştığını, ancak gürültü oranının artmasıyla performans farklılıklarının belirgin hale geldiğini göstermiştir. Özellikle ResNet-18 mimarisi, düşük SNR koşullarında dahi yüksek sınıflandırma doğruluğunu koruyarak en başarılı model olmuştur. Çalışma sonuçları, skalogram tabanlı veri üretiminin ve derin öğrenme yöntemlerinin güç kalitesi bozulmalarının sınıflandırılmasında etkili ve güvenilir bir yaklaşım sunduğunu ortaya koymaktadır.

Anahtar Kelimeler

• Güç kalitesi bozuklukları
• Sınıflandırma
• Skalogram
• Derin öğrenme

Kaynakça

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