Tren Frenleme Enerjisinin Maksimum Geri Kazanımı İçin Zaman-Planı Optimizasyonu

Öz

Bu makalede, Metro İstanbul araçlarında kullanılması için zaman planı oluşturularak maksimum enerji kazanımının optimize edilmesine yönelik yapılan araştırma sonuçları paylaşılmıştır. Bu çalışma ile amaçlanan rejeneratif enerji ile enerji kazanımı elde ederek enerji tasarrufu sağlamaktır. Rejeneratif enerji ile sağlanan enerji kazanımı, elektromanyetik frenleme yapan trenlerin ürettiği enerjiyi hatta bulunan ve hareket etmeye hazır durumunda olan diğer trenlere aktarması ilkesine dayanmaktadır. Trenin frenlemesi esnasında kinetik enerji açığa çıkar ve bu enerji elektrik enerjisine dönüştürülür, elektrik enerjisine dönüşen bu enerji katenere geri iletilir. Katener de hatta alıcı durumunda bulunan diğer bir trene bu enerjiyi iletir ve böylelikle hatta alıcı durumundaki tren bu enerjiyi kullanması ile enerji kazanımı sağlanmış olur. Rejeneratif frenleme enerjisinden en etkin bir şekilde yararlanmanın yollarından biri zaman-planı en iyileştirmesi (optimizasyonu) uygulanmasıdır. Zaman-planı en iyileştirilmesi yapılarak maksimum enerji kazanımı sağlayacak istasyon bekleme süreleri bulunur. Trenlerin istasyondaki en uygun bekleme sürelerini bulmak için bu çalışmada genetik algoritma kullanılmıştır. Genetik algoritma, çok boyutlu ve karmaşık arama uzayında en iyinin hayatta kalması ilkesine dayanan arama ve en iyileme yöntemidir. Genetik algoritmalar, evrimsel süreci bilgisayar ortamında taklit ederek problemlerin çözümünü ararlar. Başlangıç bireyleri tanımlanan kısıtlamalara dikkat edilerek rastgele oluşturulmuştur. Uygunluk fonksiyonu en iyileştirilmiş zaman-planını verecek bekleme sürelerini içeren bireyin değerlendirmesi amacıyla kullanılmaktadır. Her yeni nesilde daha iyi bireyler elde etmek üzere, farklı yöntem ve oranlarda elitizm, çaprazlama ve mutasyon operatörleri uygulanmıştır. Optimizasyonun nihai haline ulaşması iterasyon sayısı ile sınırlandırılmıştır. Gerçekleştirilen çalışma referans olarak alınan çalışmaya kıyasla %30 oranında daha iyi sonuç elde etmiştir. Referans makalede %60 oranında olan enerji kazanımının, bu çalışma ile %78 oranına kadar çıkartılabildiği gözlemlenmiştir.

Anahtar Kelimeler

• Yeniden Enerji Kazanımı
• Metro Zaman Planı
• Genetik Algoritma

Time-Plan Optimization with Genetic Algorithm for Regain of Energy from Train Tracks

Abstract

In this article, the results of the research on optimizing the maximum energy gain by creating a time plan have shared for use in Metro Istanbul vehicles. The purpose of this study is to achieve energy savings by obtaining energy gain with regenerative energy. The energy recovery provided by regenerative energy is based on the principle of transferring the energy produced by the trains that make electromagnetic braking to other trains that are found and ready to move. During the braking of the train, kinetic energy is released and this energy is converted into electrical energy, this energy transformed into electrical energy is transmitted back to the catenary. The catenary transmits this energy to another train that is also in the receiver status, and thus, energy gain is achieved, by using this energy in receiver train on the line. One of the ways to utilize regenerative braking energy in the most effective way is to apply time-plan optimization. By optimizing the time-schedule that provide maximum energy gain, the station waiting times is found. Genetic algorithm was used in this study to find the most suitable waiting times of trains at the station. Genetic algorithm is the search and optimization method based on the principle of the best survival in multi-dimensional and complex search space. Genetic algorithms seek the solution of problems by imitating the evolutionary process in a computer environment. Initial individuals were randomly created by taking into consideration the defined restrictions. The fitness function is used to evaluate the individual which includes the waiting times that give the optimized time-schedule. Elitism, crossover and mutation operators in different methods and proportions have been applied to obtain better individuals in each new generation. The finalization of optimization is limited by the number of iterations. The study carried out has achieved 30% better result than the reference study. It was observed that the 60% energy gain in the reference article can be increased up to 78% with this study.

Keywords

• Regain of Energy
• Metro Time-Table
• Genetic Algorithm

Kaynakça

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