HETEROJEN VERİ KAYNAKLARI VE ZAMANSAL MODELLER İLE GÜN ÖNCESİ TRANSFORMATÖR KESİNTİ RİSKİNİN TAHMİNİ İÇİN BİR ÖNGÖRÜCÜ BAKIM ÇERÇEVESİ

Abstract

Elektrik dağıtım şebekelerinde meydana gelen plansız kesintiler, şebeke güvenilirliğini azaltmakta ve işletme maliyetlerini artırmaktadır. Bu çalışmada, İzmir ve Manisa illerinden elde edilen gerçek saha verileri kullanılarak, transformatörler için bir gün önceden arıza riski tahmini yapan kestirimci bakım odaklı bir tahminleme modeli önerilmiştir. Çalışmada, 34 transformatöre ait 2021–2024 yılları arasındaki 8.671 kesinti kaydı, bölgesel meteorolojik veriler ve varlık odaklı teknik bilgilerle birleştirilmiştir. Önerilen sistem; aykırı değer tespiti, zaman serisi tabanlı özellik mühendisliği ve dengesiz sınıf problemleri için SMOTE, rastgele örnekleme ve sınıf ağırlıklandırma gibi yöntemleri içermektedir. Ayrıca, Min–Max ve Robust ölçekleme yaklaşımları uygulanmıştır. Makine öğrenmesi algoritmaları (Rastgele Orman, XGBoost, LightGBM, CatBoost) ile derin öğrenme modelleri (MLP, LSTM, GRU, Transformer), Optuna ile hiper parametre ayarı yapılarak eğitilmiştir. Zaman serisi çapraz doğrulama ve 2024 yılının son çeyreği için ayrılmış test seti ile değerlendirilen modelin ana başarı ölçütü G-Mean olmuştur. Sonuçlar, gradyan artırmalı modellerin genel doğruluk açısından üstün olduğunu, sıralı modellerin ise mevsimsellik içeren durumlarda daha başarılı olduğunu göstermektedir. Model, SAIDI ve SAIFI gibi güvenilirlik göstergelerinin iyileştirilmesi için etkili bakım planlaması imkânı sunmaktadır.

Keywords

• Dağıtım Şebekeleri
• Kestirimci Bakım
• Makine Öğrenmesi
• Transformatör Arızası
• Zaman Serisi Modelleme

Thanks

Bu çalışmada kullanılan veri setlerini bizlere sağlayan GDZ Elektrik Dağıtım şirketine en içten teşekkürlerimizi sunarız. Araştırmamız boyunca gösterdikleri destek son derece değerli olmuştur.

A PREDICTIVE MAINTENANCE FRAMEWORK FOR DAY-AHEAD TRANSFORMER OUTAGE RISK ESTIMATION USING HETEROGENEOUS DATA AND TEMPORAL MODELS

Abstract

Unplanned outages in power distribution networks significantly affect reliability and increase operational costs. This study introduces a predictive maintenance framework for estimating transformer outage risks one day in advance, based on real-world data from İzmir and Manisa. The dataset comprises 8,671 outage records (2021–2024) across 34 transformers, enriched with meteorological data and asset-specific metadata. The proposed pipeline includes outlier detection, temporal feature engineering, and class imbalance handling (SMOTE, undersampling, and class weighting) under Min–Max and Robust scaling. Various machine learning (Random Forest, XGBoost, LightGBM, CatBoost) and deep learning models (MLP, LSTM, GRU, Transformer) were trained with hyperparameter tuning via Optuna. Performance was evaluated using time-series cross-validation and an out-of-sample test set for Q4 of 2024, primarily with the G-Mean metric. Results reveal that gradient boosting models provide balanced accuracy, while sequence-based models are more effective under seasonal variations. The findings suggest that the integration of weather and asset data enhances predictive performance and supports proactive maintenance strategies for improving SAIDI and SAIFI reliability metrics.

Keywords

• Distributin Networks
• Machine Learning
• Predictive Maintenance
• Time-Series Modeling
• Transformer Outage

Thanks

We would like to express our sincere gratitude to GDZ Electricity Distribution for providing us with the datasets used in this study. Their support throughout our research has been invaluable.

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