Machine Learning-Based Detection of Non-Technical Losses in Power Distribution Networks

Abstract

This study focuses on the serious sustainability and reliability problem caused by non-technical losses (NTL) due to energy theft in electrical grid systems. In order to reduce these losses, we propose an artificial intelligence-based approach that utilizes deep learning architectures in the detection of different types of leakage (voltage leakage, current leakage and voltage-current leakage). Unlike the studies in the literature, the data set is converted into two-dimensional matrices and analyzed with today's popular approaches, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models; CNN surpassed LSTM's 64.17% accuracy rate with 97.50% accuracy rate. In addition, from the classical methods, 67.5 accuracy rate was obtained with the k-Nearest Neighbor (k-NN) method and 62.25 accuracy rate was obtained with the Support Vector Machines (SVM) method. Comparisons with such traditional methods have revealed the superiority of CNN in determining complex leakage patterns. The findings highlight the potential of CNN to be used as a reliable tool for real-time theft detection by integrating it into smart grid systems. Future research will aim to further increase the scalability and effectiveness of this solution by examining the integration of real-time data and hybrid model approaches.

Keywords

• Non-technical losses (NTL)
• Electricity theft detection
• Deep learning
• Convolutional neural network(CNN)
• SmartGrid

Ethical Statement

There is no need to obtain ethics committee permission for the article prepared. "There is no conflict of interest with any person/institution in the article prepared.

Thanks

I would like to thank Diyarbakır Organized Industrial Zone Directorate for providing support during the conduct of this study.

Dağıtım Şebekelerinde Teknik Olmayan Kayıpların Makine Öğrenme Yöntemleriyle Tespiti

Abstract

Bu çalışma, elektrik şebeke sistemlerinde enerji hırsızlığından kaynaklanan teknik olmayan kayıpların (NTL) oluşturduğu ciddi sürdürülebilirlik ve güvenilirlik sorununa odaklanmaktadır. Bu kayıpları azaltmak amacıyla, farklı kaçak türlerinin (gerilim kaçağı, akım kaçağı ve gerilim-akım kaçağı) tespitinde derin öğrenme mimarilerinden yararlanan yapay zeka tabanlı bir yaklaşım öneriyoruz. Literatürdeki çalışmalardan farklı olarak veri seti iki boyutlu matrislere dönüştürülerek, günümüzün popüler yaklaşımları olan Convolutional Neural Network (CNN) ve Long Short-Term Memory (LSTM) modelleri ile analiz edilmiş; CNN, %97,50 doğruluk oranı ile LSTM'nin %64,17 doğruluk oranını geride bırakmıştır. Ayrıca, klasik yöntemlerden, k-En Yakın Komşu (k-NN) yöntemi ile 67,5 doğruluk oranı ve Destek Vektör Makineleri (SVM) yöntemi ile 62,25 doğruluk oranı elde edilmiştir. Bu gibi geleneksel yöntemlerle yapılan karşılaştırmalar, CNN'in karmaşık kaçak desenlerini belirlemedeki üstünlüğünü ortaya koymuştur. Bulgular, CNN'in akıllı şebeke sistemlerine entegre edilerek gerçek zamanlı hırsızlık tespiti için güvenilir bir araç olarak kullanılma potansiyelini vurgulamaktadır. Gelecekteki araştırmalar, gerçek zamanlı verilerin entegrasyonunu ve hibrit model yaklaşımlarını inceleyerek bu çözümün ölçeklenebilirliğini ve etkinliğini daha da artırmayı hedefleyecektir.

Keywords

• Teknik olmayan kayıplar (TOK)
• Kaçak elektrik tespiti
• Derin öğrenme
• Konvolüsyonel sinir ağı (CNN)
• Akıllı şebeke

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