Electricity Distribution Networks Fault Prediction with Machine Learning Algorithms

Ali Geyikoğlu; Mete Yağanoğlu

Makale detayları

Electricity Distribution Networks Fault Prediction with Machine Learning Algorithms

Yazarlar:

Ali Geyikoğlu | Mete Yağanoğlu

Yayıncı:

Giresun University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer) | Enerji Sistemleri Mühendisliği (Diğer)

Dergi:

Karadeniz Fen Bilimleri Dergisi Vol.15 Issue 1, pp.73-98

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001342302

pISSN:

2564-7377

Anahtar kelimeler:

Özet:

 Malfunction in electrical distribution networks; They are defined as factors that prevent quality and continuous energy flow. After the failure occurs, Electricity Distribution Companies carry out corrective actions through maintenance-repair and investment works. Failures that occur and subsequent corrective actions and technical quality parameters are created by the systems. However, the resulting technical data is not used in any forecasting infrastructure, and corrective actions are generally carried out based on comments and requests. In this study, in order to avoid intuitive approaches, the Interruption Duration and Frequency data of Aras EDAŞ, which were sampled and recorded by the systems after the field activities of the electricity distribution company operators, and the main meteorological data of 7 provinces in the Aras EDAŞ operational responsibility area for the relevant periods were used. Data preprocessing, feature selection, and feature extraction were carried out on the attributes and classes in the data set. The data sets that will be used for estimation with regression operations were subjected to 8 different regression models, including Light Gradient Boosting Machine LGBM, Extreme Gradient Boosting XGB, Support Vector, Random Forest, Categorical Boosting, k-Nearest Neighbor, Decision Tree, and Linear, with 80 of the data being training and 20 being test data. Multi-class values of two different dependent variables on the data set were included separately in the class model, and a total of 16 regression studies were carried out for 8 different models. Hyperparameter optimization was applied to achieve the best model structure. While the best model accuracy for primary multi-class regression prediction was obtained as 93.305 with the LGBM Regressor, the best model accuracy for secondary multi-class prediction was obtained as 95.812 with the XGB Regressor.

 Malfunction in electrical distribution networks; They are defined as factors that prevent quality and continuous energy flow. After the failure occurs, Electricity Distribution Companies carry out corrective actions through maintenance-repair and investment works. Failures that occur and subsequent corrective actions and technical quality parameters are created by the systems. However, the resulting technical data is not used in any forecasting infrastructure, and corrective actions are generally carried out based on comments and requests. In this study, in order to avoid intuitive approaches, the Interruption Duration and Frequency data of Aras EDAŞ, which were sampled and recorded by the systems after the field activities of the electricity distribution company operators, and the main meteorological data of 7 provinces in the Aras EDAŞ operational responsibility area for the relevant periods were used. Data preprocessing, feature selection, and feature extraction were carried out on the attributes and classes in the data set. The data sets that will be used for estimation with regression operations were subjected to 8 different regression models, including Light Gradient Boosting Machine LGBM, Extreme Gradient Boosting XGB, Support Vector, Random Forest, Categorical Boosting, k-Nearest Neighbor, Decision Tree, and Linear, with 80 of the data being training and 20 being test data. Multi-class values of two different dependent variables on the data set were included separately in the class model, and a total of 16 regression studies were carried out for 8 different models. Hyperparameter optimization was applied to achieve the best model structure. While the best model accuracy for primary multi-class regression prediction was obtained as 93.305 with the LGBM Regressor, the best model accuracy for secondary multi-class prediction was obtained as 95.812 with the XGB Regressor.

 Malfunction in electrical distribution networks; They are defined as factors that prevent quality and continuous energy flow. After the failure occurs, Electricity Distribution Companies carry out corrective actions through maintenance-repair and investment works. Failures that occur and subsequent corrective actions and technical quality parameters are created by the systems. However, the resulting technical data is not used in any forecasting infrastructure, and corrective actions are generally carried out based on comments and requests. In this study, in order to avoid intuitive approaches, the Interruption Duration and Frequency data of Aras EDAŞ, which were sampled and recorded by the systems after the field activities of the electricity distribution company operators, and the main meteorological data of 7 provinces in the Aras EDAŞ operational responsibility area for the relevant periods were used. Data preprocessing, feature selection, and feature extraction were carried out on the attributes and classes in the data set. The data sets that will be used for estimation with regression operations were subjected to 8 different regression models, including Light Gradient Boosting Machine LGBM, Extreme Gradient Boosting XGB, Support Vector, Random Forest, Categorical Boosting, k-Nearest Neighbor, Decision Tree, and Linear, with 80 of the data being training and 20 being test data. Multi-class values of two different dependent variables on the data set were included separately in the class model, and a total of 16 regression studies were carried out for 8 different models. Hyperparameter optimization was applied to achieve the best model structure. While the best model accuracy for primary multi-class regression prediction was obtained as 93.305 with the LGBM Regressor, the best model accuracy for secondary multi-class prediction was obtained as 95.812 with the XGB Regressor.

 Elektrik dağıtım şebekelerinde arıza; kaliteli ve sürekli enerji akışını engelleyici faktörler olarak tanımlanmaktadır. Arızanın meydana gelmesi sonrasında Elektrik Dağıtım Şirketleri, bakım-onarım ve yatırım çalışmaları ile düzeltici faaliyetler gerçekleştirmektedir. Meydana gelen arızalar ve sonrası düzeltici faaliyetler ile teknik kalite parametreleri sistemlerce oluşturulmaktadır. Ancak ortaya çıkan teknik veriler, herhangi bir tahminleme altyapısında kullanılmamakta, düzeltici faaliyetler genel olarak yorum ve taleplere istinaden gerçekleştirilmektedir. Bu çalışmada, sezgisel yaklaşımların önüne geçmek amacıyla, elektrik dağıtım şirketi operatörlerinin saha faaliyetleri sonrası sistemler tarafından örneklenerek kayıt altına alınan Aras EDAŞa ait Kesinti Süreleri ve Sıklığı verileri ile ilgili dönemlere ait Aras EDAŞ işletme sorumluluk sahasındaki 7 ile esas meteorolojik veriler kullanılmıştır. Veri seti içerisinde yer alan öznitelikler ve sınıflar üzerinde veri ön işleme, öznitelik seçimi, öznitelik çıkarımı gerçekleştirilmiştir. Regresyon işlemleri ile tahminleme gerçekleştirilecek hale gelen veri setleri 80i eğitim ve 20si test verisi olacak şekilde; Hafif Gradyan Artırma Makinesi LGBM, Aşırı Gradyan Artırma XGB, Destek Vektör, Rastgele Orman, Kategorik Artırma, k-En Yakın Komşu, Karar Ağacı, Lineer olmak üzere 8 farklı regresyon modeline tabi tutulmuştur. Veri seti üzerinde yer alan iki farklı bağımlı değişkene ait çok sınıflı değerler ayrı ayrı sınıf modeline dahil edilmiş olup toplamda 8 farklı model için 16 adet regresyon çalışması gerçekleştirilmiştir. En iyi model yapısına ulaşabilmek amacıyla hiperparametre optimizasyonu uygulanmıştır. Birincil çok sınıflı regresyon tahmini için en iyi model doğruluğu LGBM Regressor ile 93,305 olarak elde edilirken, ikincil çok sınıflı tahmin için en iyi model doğruluğu XGB Regressor ile 95,812 olarak elde edilmiştir.

Makale detayları

Electricity Distribution Networks Fault Prediction with Machine Learning Algorithms

Yazarlar:

Ali Geyikoğlu | Mete Yağanoğlu

Yayıncı:

Giresun University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer) | Enerji Sistemleri Mühendisliği (Diğer)

Dergi:

Karadeniz Fen Bilimleri Dergisi Vol.15 Issue 1, pp.73-98

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001342302

pISSN:

2564-7377

Electricity distribution network failure

Machine learning

Regression

PredictionElektrik dağıtım şebeke arızası

Makine öğrenmesi

Regresyon

Tahmin

Sayı

Makale detayları

Yazarlar: Ali Geyikoğlu | Mete Yağanoğlu

Yayıncı: Giresun University

Ana Başlıklar: Yazılım Mühendisliği (Diğer) | Enerji Sistemleri Mühendisliği (Diğer)

Dergi: Karadeniz Fen Bilimleri Dergisi Vol.15 Issue 1, pp.73-98

Belge Tipi: Makale

Yayın Tarihi: 3/2025

Belge Dili: Türkçe

Document ID (Org No.): 2001342302

pISSN: 2564-7377

Sayı

Yazarlar:

Ali Geyikoğlu | Mete Yağanoğlu

Yayıncı:

Giresun University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer) | Enerji Sistemleri Mühendisliği (Diğer)

Dergi:

Karadeniz Fen Bilimleri Dergisi Vol.15 Issue 1, pp.73-98

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001342302

pISSN:

2564-7377