YSA Ve DVM Yöntemlerinin Bir Metro Hattında Gerilim Düşümünün Tahmini İçin Karşılaştırılması

Year 2018, Volume: 10 Issue: 1, 56 - 65, 29.01.2017

İlhan Kocaarslan , Mehmet taciddin Akçay , Abdurrahim Akgündoğdu , Hasan Tiryaki

https://doi.org/10.29137/umagd.352946

Cited By: 2

Abstract

Bu çalışmada, 1500 V DC beslemeli bir raylı sistemde cer gücünün meydana getirdiği gerilim düşümünün maksimum değeri Yapay Sinir Ağları (YSA) ve Destek Vektör Makineleri (DVM) yardımıyla belirlenmiştir. YSA ve DVM yöntemleriyle hatta oluşan gerilim düşümü işletmesel parametrelere bağlı olarak hesaplanmıştır. YSA ve DVM teknikleri açıklanarak elde edilen sonuçlar karşılaştırılmıştır. YSA modeli için levenberg marquardt (LM) algoritması kullanılmıştır. Levenberg-Marquardt algoritması yapay sinir ağlarının eğitiminde sağladığı hız ve kararlılık nedeni ile tercih edilmektedir. Raylı sistemlerde elektrifikasyon sistemi işletmesel verilere ve hat parametrelerine bağlı olarak tasarlanmaktadır. Elektrifikasyon sistemi oluşturulurken işletme esnasında cer gücünün gereksinimi olan minimum besleme gerilim değerinin sağlanması gerekmektedir. Cer gücü geriliminin en düşük değerini hatta oluşan gerilim düşümünün en yüksek değeri belirlemektedir. Bu değerin işletme sürekliliği için belirli limitler içinde tutulması gerekmektedir. Benzetim için tek yönlü ve çift yönlü beslenme durumlarına ait oluşturulan veriler incelenmiştir. Bu çalışma ile demiryolu elektrifikasyon sistemine ait cer gücü simülasyonuna ait sonuçlar yapay zeka yoluyla tahmin edilmektedir. Bu sayede sisteme ait değişkenler farklı olsa dahi tekrar tekrar benzetim yapılmasının önüne geçilmektedir. Tasarlanan sistem ile %95 üzeri başarı oranı elde edilmiştir.

Keywords

Cer Gücü, DVM, Elektrifikasyon, Hat, Raylı Sistem, YSA

The Comparison of the ANN and SVM Methods for the Prediction of Voltage Drop on a Subway Line

Abstract

In this
study, the determination of the maximum value of the voltage drop created by
the traction force was performed for a 1500 V DC-fed rail system by means of
the artificial neural networks (ANN) and support vector machines (SVM). The
voltage drop occurring on the line was calculated with regard to the operating
parameters by means of the ANN and SVM. The ANN and SVM were explained and a
comparison was made. The Levenberg-Marquardt (LM) algorithm was used for the
ANN model. The Levenberg-Marquardt algorithm was preferred due to the speed and
stability it provides for the training of artificial neural networks. The
electrification system in the rail systems is designed with regard to the
operating data and design parameters. The minimum voltage rating that the
traction force requires during the operation needs to be provided in the
electrification system. The highest value of the voltage drop on the line
determines the lowest value of the traction force voltage. This value must be in
certain limits for the continuity of the operation. The created datas regarding
one-way and two-way supply conditions were examined for simulation. In this way
railway electrification traction power simulation was done by artificial
intelligence methods and traction power simulation results were predicted
without doing traction simulation. With the designed system, the success rate
is over %95.

Keywords

ANN, Electrification, Line, Rail System, SVM, Traction Force

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