Comparison of ANN and ANFIS Methods for the Voltage-Drop Prediction on an Electric Railway Line

Year 2018, Volume: 18 Issue: 1, 26 - 35, 23.02.2018

İlhan Kocaarslan Mehmet Taciddin Akçay Abdurrahim Akgündoğdu Hasan Tiryaki

Abstract

Railway electrification
systems are designed with regard to the operating data and design parameters.
The minimum voltage rating required by traction during the operation should be
provided. The maximum voltage drop on a line determines the minimum traction
voltage. This voltage should be maintened within certain limits for the
continuity of operation. In this study, the maximum voltage drop generated via
traction was determined using artificial neural network (ANN) and adaptive
neuro-fuzzy inference system (ANFIS) for a 25-kV AC-supplied railway. The
voltage drop on line was calculated with regard to the operating data using ANN
and ANFIS. ANN and ANFIS were explained, and the results were compared. The
Levenberg–Marquardt (LM) algorithm was used for the ANN model. The LM algorithm
is preferred because of the speed and stability it provides for the training of
ANNs. The data created for one-way supply status were examined for simulation. 

Keywords

ANFIS, ANN, electrification, railway, traction

References

• 1. J. S. Huh, H.S. Shin, W. S. Moon, B. W. Kang, J. C. Kim, “Study on voltage unbalance improvement using SFCL in power feed network with electric railway system,” IEEE Transactions on Applied Superconductivity, vol. 3: 3601004, 2013. 2. A. Ghassemi, S. S. Fazel, I. Maghsoud, S. Farshad, “Comprehensive study on the power rating of a railway power conditioner using thyristor switched capacitor,” IET Electrical Systems in Transportation, vol. 4, pp. 97-106, 2014. 3. G. Raimondo, P. Ladoux, A. Lowinsky, H. Caron, P. Marino, “Reactive power compensation in railways based on AC boost choppers,” IET Electrical Systems in Transportation, vol. 2, pp. 169-177, 2012. 4. K. Aodsup and T. Kulworawanichpong, “Effect of train headway on voltage collapses in high-speed AC railways,” In: APPEEC 2012 Power and Energy Engineering Conference; 27-29 March 2012; Shanghai, China. New York, USA: IEEE. pp. 1-4. 5. M. A. A. Baseri, M. N. Nezhad, M. A. Sandidzadeh, “Compensating procedures for power quality amplification of AC electrified railway systems using FACTS,” In: PEDSTC 2011 Power Electronics Drive Systems and Technologies Conference; 16-17 Februrary 2011; Tehran, Iran. New York, USA: IEEE. pp. 518-521. 6. M. Brenna and F. Foiadelli, “The compatibility between DC and AC supply of the Italian railway system,” In: Power and Energy Society General Meeting; 24-29 July 2011; San Diego, USA. New York, USA: IEEE. pp. 1-7. 7. L. Abrahamsson, T. Kjellqvist, S. Ostlund, “High-voltage DC-feeder solution for electric railways,” IET Power Electronics, vol. 5, pp. 1776-1784, 2012. 8. S. V. Raygani, A. Tahavorgar, S. S. Fazel, B. Moaveni, “Load flow analysis and future development study for an AC electric railway,” IET Electrical Systems in Transportation, vol. 2, pp. 139-147, 2012. 9. C. J. Goodman and M. Chymera, “Modelling and simulation,” In: REIS 2013 Railway Electrification Infrastructure and Systems Conference; 3-6 June 2013; London, England. New York, USA: IEEE. pp. 16-25. 10. P. Ladoux, G. Raimondo, H. Caron, P. Marino, “Chopper-Controlled steinmetz circuit for voltage balancing in railway substations,” IEEE Transactions on Power Electronics, vol. 28, pp. 5813-5822, 2013. 11. H. S. Shin, S. M. Cho, J. C. Kim, “Protection scheme using SFCL for electric railways with automatic power changeover switch system,” IEEE Transactions on Applied Superconductivity, vol. 20, 5600604, 2012. 12. H. S. Shin, S. M. Cho, J. S. Huh, J. C. Kim, D. J. Kweon, “Application on of SFCL in automatic power changeover switch system of electric railways,” IEEE Transactions on Applied Superconductivity vol. 22, 5600704, 2012. 13. V. Kolar, R. Hrbac, T. Mlcak T, “Measurement and simulation of stray currents caused by AC railway traction,” In: EPE 2015 Electric Power Engineering Conference; 20-22 May 2015; Prague, Czech Republic. New York, USA: IEEE. pp. 764-768. 14. M. Chen, W. Jiang, J. Luo, T. Wen, “Modelling and simulation of new traction power supply system in electrified railway,” In: ITSC 2015 IEEE 18th International Conference on Intelligent Transportation Systems; 15-18 September 2015; Las Palmas, Canada. New York, USA: IEEE. pp. 1345-1350. 15. M. Soler, J. Lopez, J. Manuel, M. S. Pedro, J. Maroto, “Methodology for multiobjective optimization of the AC railway power supply system,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, pp. 2531-2542, 2015. 16. Z. He, Y. Zhang, S. Gao, “Harmonic resonance assessment to traction power supply system considering train model in China high-speed railway,” IEEE Transactions On Power Delivery, vol. 29, pp. 1735-1743, 2014. 17. W. Song, J. Ma, L. Zhou, X. Feng, “Deadbeat predictive power control of single-phase three-level neutral-point-clamped converters using space-vector modulation for electric railway traction,” IEEE Transactıons On Power Electronics, vol. 31, pp. 721-732, 2016. 18. M. Shafighy, S. Khoo, A. Z. Kouzani, “Modelling and simulation of regeneration in AC traction propulsion system of electrified railway,” IET Electrical Systems in Transportation, vol. 5, pp. 145-155, 2015. 19. S. Kejian, W. Mingli, V. G. Agelidis, W. Hui W, “Line current harmonics of three-level neutral point-clamped electric multiple unit rectifiers: analysis,” Simulation and Testing, IET Power Electronics, vol. 7, 1850-1858, 2014. 20. P. Drabek, Z. Peroutka, M. Pittermann, P. Cedl, “New configuration of traction converter with medium-frequency transformer using matrix converters,” IEEE Transactıons On Industrıal Electronics, vol. 58, pp. 5041-5048, 2011. 21. H. Ozdemir, “Artificial neural networks and their usage in weaving technology,” Electronic Journal of Textile Technologies, vol. 7, pp. 51-68, 2013. 22. M. Sahin, F. Buyuktumturk, Y. Oguz, “Light quality control with artificial neural networks,” Afyon Kocatepe University Journal of Science and Engineering, vol. 13, vol. 1-10, 2013. 23. R. Bayindir, Ö. Sesveren, “Design of a visual interface for ANN based systems,” Pamukkale University Engineering Faculty Journal of Engineering Science, vol. 14, pp. 101-109, 2008. 24. D. Askin, I. Iskender, A. Mamızadeh, “Dry type transformer winding thermal analysis usıng different neural network methods,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 26, pp. 905-913, 2011. 25. M. A. Cavuslu, Y. Becerikli, C. Karakuzu. “Hardware implementation of neural network training with levenberg-marquardt algorithm,” Journal of Computer Science and Engineering, vol. 5, pp. 31-38, 2012. 26. İ. Dalkiran, K. Danisman, “Artificial neural network based chaotic generator for cryptology,” Turkish Journal Of Electrical Engineering And Computer Sciences, vol. 18, pp. 225-240, 2010. 27. M. Ceylan, Y. Ozbay, O. N. Ucan, E. Yildirim, “A novel method for lung segmentation on chest ct images: complex-valued artificial neural network with complex wavelet transform,” Turkish Journal Of Electrical Engineering And Computer Sciences, vol. 18, pp. 613-623, 2010. 28. S. Partal, İ. Senol, A. F. Bakan, K. N. Bekiroglu, “Online speed control of a brushless AC servomotor based on artificial neural networks,” Turkish Journal Of Electrical Engineering And Computer Sciences, vol. 19, pp. 373-383, 2011. 29. S. Jashfar, S. Esmaeili, M. Z. Jahromi, M. Rahmanian, “Classification of power quality disturbances using s-transform and tt-transform based on the arti_cial neural network,” Turkish Journal Of Electrical Engineering And Computer Sciences, vol. 21, pp. 1528-1538, 2013. 30. M. Afsharizadeh, M. Mohammadi, “Prediction-Based reversible ımage watermarking using artificial neural networks,” Turkish Journal Of Electrical Engineering And Computer Sciences, vol. 24, pp. 896-910, 2016. 31. I. A. Ozkan, I. Saritas, S. Herdem, “Modeling of magnetic filtering with ANFIS,” In: 12. National Conference on Electrical Electronic Computer Biomedical Engineering; 14-18 November 2007; Eskişehir, Turkey. Ankara, Turkey: CEE. pp. 415-418. 32. M. R. Minaz, A. Gun, M. Kurban, N. Imal, “Estimation of pressure, temperature and wind speed of bilecik using different methods,” Gaziosmanpasa Journal of Scientific Research, vol. 3, pp. 100-111, 2013. 33. S. Sit, H. R. Ozcalik, E. Kilic, O. Dogmus, M. Altun, “Investigation of performance based on online adaptive neuro-fuzzy ınference system (ANFIS) for speed control of ınduction motors,” Cukurova University Journal of the Faculty of Engineering and Architecture, vol. 31, pp. 33-42, 2016. 34. J. S. R. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, pp. 665–685, 1993. 35. S. Yurtcu and A. Ozocak, “Prediction of compression index of fine-grained soils using statistical and artificial intelligence methods,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 31, pp. 597-608, 2016. 36. C. J. Willmott and C. Matsuura, “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) ın assessing average model performance,” Climate Research, vol. 30, pp. 79-82, 2005.