1. Determination of distance between DC traction power centers in a 1500-V DC subway line with artificial intelligence methods
- Author
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Mehmet Taciddin Akçay and İlhan Kocaarslan
- Subjects
Adaptive neuro fuzzy inference system ,Tractive force ,General Computer Science ,Artificial neural network ,business.industry ,Computer science ,Computer Science::Neural and Evolutionary Computation ,ANFIS,artificial neural network,DC traction power,subway electrification,support vector machine ,Mühendislik ,Power (physics) ,Support vector machine ,Traction power network ,Engineering ,Line (geometry) ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Voltage drop - Abstract
The electrification system in rail systems is designed with regard to the operating data and design parameters. While the electrification system is formed, the minimum voltage rating that the traction force requires during the operation needs to be provided. The highest value of the voltage drop occurring on the line is determined by the distance between power centers. This value needs to be kept within certain limits for the continuity of operation. In this study, the determination of the distance between DC traction power centers for a 1500-V DC-fed rail system is done by means of the adaptive neuro-fuzzy inference system (ANFIS), support vector machines (SVMs), and artificial neural networks (ANNs). The distance occurring on the line is calculated with regard to the operating parameters by means of the ANFIS, SVMs, and ANNs. The ANFIS, SVMs, and ANNs are explained and a comparison is made. The data created regarding one-way and two-way supply conditions are examined for simulation. The main contribution of this paper is the determination of the distance between railway traction power centers with artificial intelligence methods.
- Published
- 2017