CONSTRUCTIVE MODELLING FOR ZONE OF RECOVERY ENERGY DISTRIBUTION OF DC TRACTION

Authors

DOI:

https://doi.org/10.15802/stp2016/84036

Keywords:

energy recovery, energy conservation, constructive-synthesizing structures, structural diagrams, model, traction substation

Abstract

Purpose.The article is aimed to develop the means and methods of forming a plurality of real and potential structural diagrams for zones of energy recovery and different locations of trains for further training neuro-fuzzy networks on the basis of expert solutions and also for the formation of good control. Methodology. Methodology of mathematical and algorithmic constructivism for modeling the structural diagrams of the electric supply system and modes of traction power consumption and the train’s locations in zones of energy recovery was applied. This approach involves the development of constructive-synthesizing structures (CSS) with transformation by specialization, interpretation, specification and implementation. Development CSS provides an extensible definition media, relations and the signature of operations and constructive axiomatic. The most complex and essential part of the axioms is the set formed by the substitution rules defining the process of withdrawal of the corresponding structures. Findings. A specialized and specified CSS, which allows considering all the possibilities and features, that supply power traction systems with modern equipment, stations and trains location was designed. Its feature: the semantic content of the terminal alphabet images of electrical traction network and power consumers with relevant attributes. A special case of the formation of the structural diagram shows the possibilities CSS in relation to this problem. Originality. A new approach to solving the problem of rational use of energy recovery, which consists in application of the methods and means of artificial neural networks, expert systems, fuzzy logic and mathematical and algorithmic constructivism. This paper presents the methods of constructive simulation of a production-distribution of energy recovery zone structure in the system of the DC traction. Practical value. The tasks decision of the rational use of energy recovery can significantly save energy, contribute to the technical re-equipment of a railway transportation of Ukraine through the introduction of modern means and capabilities. The developed model can be used to solve other energy-saving tasks in different systems of electric transport.

Author Biographies

V. I. Shynkarenko, Dnipropetrovsk National University of Railway Transport named after Academician V. Lazaryan

Dep. «Computer and Information Technologies», Lazaryan St., 2, Dnipro, Ukraine, 49010, tel. +38 (056) 373 15 35

O. I. Sablin, Dnipropetrovsk National University of Railway Transport named after Academician V. Lazaryan

Dep. «Electricity Supply of Railways», Lazaryan St., 2, Dnipro, Ukraine, 49010, tel. +38 (056) 373 15 37

O. P. Ivanov, Dnipropetrovsk National University of Railway Transport named after Academician V. Lazaryan

Dep. «Computer and Information Technologies», Lazaryan St., 2, Dnipro, Ukraine, 49010, tel. +38 (056) 373 15 35

References

Bodyanskiy, Y. V., Kucherenko, V. Y., Kucherenko, Y. I., Mikhalev, A. I., & Filatov, V. A. (2008). Gibridnyye neyro-fazzi modeli i multiagentnyye tekhnologii v slozhnykh sistemakh. Dnipropetrovsk: Sistemnyye tekhnologii.

Gorbachev, S., & Syryamkin, V. (2014). Neyro-nechetkiye metody v intellektualnykh sistemakh obrabotki i analiza mnogomernoy informatsii. Tomsk: Izdatelstvo Tomskogo universiteta.

Zakaryukin, V., Kryukov, A., & Cherepanov, A. (2015). Intellektualnyye tekhnologii upravleniya kachestvom elektroenergii. Irkutsk: IrGTU.

Kryukov, A. V., & Cherepanov, A. V. (2014). Modelirovaniye sistem tyagovogo elektrosnabzheniya, osnashchennykh nakopitelyami energii. Paper presented at XIII (XXXV) Vserossiyskoy nauchno-tekhnicheskoy konferentsii «Yestestvennyye i inzhenernyye nauki – razvitiyu regionov Sibiri», Bratsk.

Sablin, O. I., Kuznietsov, V. H., Bondar, O. I., & Artemchyk, V. V. (2014). Modeliuvannia vzaiemodii elektrorukhomoho skladu v rezhymi rekuperatsii elektroenerhii rozoseredzhenoiu systemoiu tiahovoho elektropostachannia. Elektryfikatsiia transportu – Transport Electrification, 7, 46-53.

Rutkovskaya, D., Pilinskiy, M., & Rutkovskiy, L. (2004). Neyronnyye seti, geneticheskiye algoritmy i nechetkiye sistemy. Moscow: Goryachaya liniya – Telekom.

Sablin, O. I. (2013). Analiz kachestva rekuperiruyemoy elektroenergii v sisteme elektricheskogo transporta. Visnyk Natsionalnoho tekhnichnoho universytetu «Kharkivskyi politekhnichnyi instytut», 38(1011), 187-190.

Niu, B., Zhu, Y., He, X., & Shen, H. A. (2008). Multi-Swarm Optimizer Based Fuzzy Modeling Approach for Dynamic Systems Processing. Neuro-computing, 71(7-9), 1436-1448. doi:10.1016/j.neucom.2007.05.010

Bodyanskiy, Y., & Vynokurova О., (2013). Hybrid Adaptive Wavelet-Neuro-Fuzzy System for Chaotic Time Series Identification. Information Sciences, 220, 170-179. doi:10.1016/j.ins.2012.07.044

Bodyanskiy, Y., Pliss, I., & Volkova, V. (2012). Modified Probabilistic Neuro-Fuzzy Network for Text Documents Proc. International Journal of Computing, 11(4), 391-396.

Kruse, R., Borgelt, C., Klawonn, F., Moeves, C., Steinbrecher, M., & Held, P. (2013). Computational Intelligence. A Methodological Introduction. London: Springer Verlag.

Du, K. L., & Swamy, M. N. S. (2014). Neural Networks and Statistical Learning. London: Springer Verlag. doi:10.1007/978-1-4471-5571-3

Jang, J. R., Sun, C. T., & Mizutani, E. (2010). Neuro-Fuzzy and Soft Computing – Computational Approach to Learning and Machine Intelligence. New Dehli: PHI Learning.

Jeno Paul P., & Ruban Deva Prakash T. (2011). Neuro-Fuzzy Based Constant Frequency-Unified Power Quality Conditioner. International Journal of System Signal Control and Engineering Application, 4(1), 10-17. doi:10.3923/ijssceapp.2011.10.17

Kori, A. K., Sharma, A. K., & Bhadoriya, A. K. S. (2012). Neuro Fuzzy System Based Condition Monitoring of Power Transformer. International Journal of Computer Science, 9(2), 495-499.

Kosko, B. (1992). Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. NJ: Prentice Hall.

Mukherjee, V., & Ghoshal, S. P. (2007). Intelligent Particle Swarm Optimized Fuzzy PID Controller for AVR System. Electric Power Systems Research, 77(12), 1689-1698. doi:10.1016/j.epsr.2006.12.004

Nasri, A., Fekri Moghadam M., & Mokhtari, H. (2010). Timetable Optimization for Maximum Usage of Regenerative Energy of Braking in Electrical Railway Systems. Paper presented at Int. Symposium on Power Electronics, Electrical Drives, Automation and Motion, Pisa.

Shynkarenko, V. I., & Ilman, V. M. (2014). Constructive-Synthesizing Structures and Their Grammatical Interpretations. I. Generalized Formal Constructive-Synthesizing Structure. Cybernetics and Systems Analysis, 50(5), 655-662. doi:10.1007/s10559-014-9655-z

Shynkarenko, V. I., Ilman, V. M., & Skalozub, V. V. (2009). Structural Models of Algorithms in Problems of Applied Programming. I. Formal Algorithmic Structures. Cybernetics and Systems Analysis, 45(3), 329-339. doi:10.1007/s10559-009-9118-0

Sood, A., & Aggarwal, S. (2011). Crossroads in Classification: Comparison and Analysis of Fuzzy and Neuro-Fuzzy Techniques. International Journal of Computer Applications, 24, 13-17. doi:10.5120/2924-3866

Kacprzyk, J., & Pedrycz, W. (2015). Springer Handbook of Computational Intelligence. Berlin-Heidelberg: Springer-Verlag. doi:10.1007/978-3-662-43505-2

Published

2016-10-25

How to Cite

Shynkarenko, V. I., Sablin, O. I., & Ivanov, O. P. (2016). CONSTRUCTIVE MODELLING FOR ZONE OF RECOVERY ENERGY DISTRIBUTION OF DC TRACTION. Science and Transport Progress, (5(65), 125–135. https://doi.org/10.15802/stp2016/84036

Issue

Section

INFORMATION AND COMMUNICATION TECHNOLOGIES AND MATHEMATICAL MODELING