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Application of ant-colony algorithm to the issue of improving rectified voltage parameters in electric tram traction

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, the problem related to transformation of ac voltage into DC voltage used in tram supply is considered. A variable component is always present in rectified voltage. Pulsation of rectified voltage is influenced by different factors. In a 12-pulse system, where two secondary transformer windings are used (one delta-connected and the other star-connected), an additional factor increasing the pulsation is the unbalance of the output voltages at these windings. Tap changer may be used and its setting is optimized here by applying the ant-colony algorithm. Different supply voltage variants have been considered. It is demonstrated that pulsation may be reduced by even 25%.
Czasopismo
Rocznik
Strony
133--144
Opis fizyczny
Bibliogr. 45 poz.
Twórcy
autor
  • Silesian University of Technology Akademicka 2, 44-100 Gliwice, Poland
autor
  • Silesian University of Technology Akademicka 2, 44-100 Gliwice, Poland
autor
  • Silesian University of Technology Akademicka 2, 44-100 Gliwice, Poland
Bibliografia
  • 1. Dantzig, G.B. Origins of simplex method, in: A history of scientific computing. ACM New York, NY. USA. 1990. P. 358.
  • 2. Holland, J. Outline of control parameters for genetic algorithms. Journal of Association for Computing Machinery. 1962. Vol. 3. P. 297-314.
  • 3. Edmonds, J. Matroids and the greedy algorithm. Mathematical programming. 1971. Vol. 1(1). P. 127-136.
  • 4. Glover, F. Future paths for integer programming and links to artificial intelligence. Computers & operations research. 1986. Vol. 13(5). P. 533-549.
  • 5. Feo, T.A. & Resende, M.GC. Greedy randomized adaptive search procedures. Journal of global optimization. 1995. Vol. 6(2). P. 109-133.
  • 6. Martin, O. & Otto, S.W. & Felten, E. W. Large-step Markov chains for the TSP incorporating local search heuristics. Operations Research Letters. 1992. Vol. 11(4). P. 219-224.
  • 7. Baum, E.B. Towards practical ‘neural’ computation for combinatorial optimization problems. In: AIP Conference Proceedings. AIP. 1986. P. 53-58.
  • 8. Koza, John R. Genetic programming: on the programming of computers by means of natural selection. MIT press. 1992.
  • 9. Dorigo, M. Optimization, Learning and Natural Algorithms‖ (in Italian). PhD thesis. Politecnico di Milano. Italy, 1992.
  • 10.Eberhart, R. & Kennedy, J. A new optimizer using particle swarm theory. In: Micro Machine and Human Science, 1995. MHS'95. Proceedings of the Sixth International Symposium on. IEEE, 1995. P. 39-43.
  • 11.Voudouris, C. & Tsang, E. Guided local search and its application to the traveling salesman problem. European journal of operational research. 1999. Vol. 113(2). P. 469-499.
  • 12.Karaboga, D. & Basturk, B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization. 2007. Vol. 39(3). P. 459-471.
  • 13.Kennedy, J. Particle swarm optimization. In: Encyclopedia of machine learning. Springer US. 2011. P. 760-766.
  • 14.Karaboga, D. & Gorkemli, B. & Ozturk, C. & Karaboga, N. A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review. 2014. Vol. 42(1). P. 21-57.
  • 15.Karaboga, D. & Basturk, B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization. 2007. Vol. 39(3). P. 459-471.
  • 16.Storn, R. & Price, K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization. 1997. Vol. 11(4). P. 341-359.
  • 17.Müller, S.D. & Marchetto, J. & Airaghi, S. & Koumoutsakos, P. Optimization based on bacterial chemotaxis. IEEE transactions on Evolutionary Computation. 2002. Vol. 6(1). P. 16-29.
  • 18.Passino, K.M. Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems. 2002. Vol. 22(3). P. 52-67.
  • 19.Krishnanand, K.N. & Ghose, D. Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Swarm intelligence symposium, 2005. SIS 2005. Proceedings 2005 IEEE. IEEE. 2005. P. 84-91.
  • 20.Yang, X.-S. A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010). 2010. P. 65-74.
  • 21.Fister, JR, Iztok, et al. A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186, 2013.
  • 22.Hu, X. & Eberhart, R.C. & Shi, Y. Engineering optimization with particle swarm. In: Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE. IEEE. 2003. P. 53-57.
  • 23.Birge, B. PSOt-a particle swarm optimization toolbox for use with Matlab. In: Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE. IEEE. 2003. P. 182-186.
  • 24.Lee, T.-Y. Operating schedule of battery energy storage system in a time-of-use rate industrial user with wind turbine generators: a multipass iteration particle swarm optimization approach. IEEE Transactions on Energy Conversion. 2007. Vol. 22(3). P. 774-782.
  • 25.Lee, C.-S. & Ayala, H.V.H. & Dos Santos Coelho, L. Capacitor placement of distribution systems using particle swarm optimization approaches. International Journal of Electrical Power & Energy Systems. 2015. Vol. 64. P. 839-851.
  • 26.Rahman, I., et al. Review of recent trends in optimization techniques for plug-in hybrid, and electric vehicle charging infrastructures. Renewable and Sustainable Energy Reviews. 2016. Vol. 58. P. 1039-1047.
  • 27.Kumar, D., et al. Reliability-constrained based optimal placement and sizing of multiple distributed generators in power distribution network using cat swarm optimization. Electric Power Components and Systems. 2014. Vol. 42(2). P. 149-164.
  • 28.Mohammadi, H.R. & Akhavan, A. Parameter estimation of three-phase induction motor using hybrid of genetic algorithm and particle swarm optimization. Journal of Engineering. 2014.
  • 29.Zhang, Y. & Wang, S. & Ji, G. A comprehensive survey on particle swarm optimization algorithm and its applications. Mathematical Problems in Engineering. 2015.
  • 30.Yang, X.-S., et al. (ed.). Metaheuristics in water, geotechnical and transport engineering. Newnes. 2012.
  • 31.Poli, R. Analysis of the publications on the applications of particle swarm optimisation. Journal of Artificial Evolution and Applications. 2008.
  • 32.W, Q. & Cole, C. & Mcsweeney, T. Applications of particle swarm optimization in the railway domain. International Journal of Rail Transportation. 2016. Vol. 4(3). P. 167-190.
  • 33.PN-EN 60076-1:2011 - wersja angielska. Transformatory. Wymagania ogólne. Warszawa: Polski Komitet Normalizacyjny. 75 p. [In Polish: Transformers. General requirements. Warsaw: Polish Committee of Standardization].
  • 34.Jabłoński, M. Transformatory. Łódź: Wyd. Politechniki Łódzkiej. 1994. [In Polish: Transformers. Łódź: Łódź University of Technology ed.].
  • 35.Jezierski, E. & Gogolewski, Z. & Kopczyński, Z. & Szmit, J. Transformatory, budowa I projektowanie. Warszawa: WNT. 1963. [In Polish: Transformers, construction and design. Warsaw: WNT].
  • 36.Mizia, W. Transformatory. Gliwice: Wydawnictwo Politechniki Śląskiej.1996. [In Polish: Transformers. Gliwice: Silesian University of Technology ed.].
  • 37.Glinka, T. Maszyny elektryczne i transformatory. Podstawy teoretyczne, eksploatacja i diagnostyka. Sosnowiec: Wyd. INiME KOMEL. 2015. [In Polish: Electrical machines and transformers. Theoretical basics, operation and diagnostics. Sosnowiec: KOMEL Institute ed.].
  • 38.Sikora, A. & Kulesz, B. & Grzenik, R. Dwunastopulsowe i dwudziestoczteropulsowe układy przetwarzania napięcia przemiennego na napięcie stałe. Prace Naukowe Politechniki Śląskiej. Elektryka. 2015. Vol. 3. P. 29-64. [In Polish: 12-pulse and 24-pulse ac/dc voltage transformation circuits. Scientific Journal of Silesian University of Technology, Elektryka].
  • 39.Sikora, A. & Kulesz, B. Transformatory prostownikowe podstacji trakcyjnej-poszukiwanie najkorzystniejszego rozwiązania. Maszyny Elektryczne: zeszyty problemowe. 2009. Vol. 82. P. 181-185. [In Polish: Rectifier transformers of traction substation – searching for most favourable design. Electrical Machines - Transaction Journal, KOMEL Institute ed.].
  • 40.Sikora, A. & Kulesz, B. Zależność jakości energii sieci trakcyjnej od zastosowanych układów transformatorów prostownikowych. Zeszyty Problemowe–Maszyny Elektryczne BOBRME Komel. 2008. Vol. 80. [In Polish: Impact of rectifier transformer design on traction network energy. Electrical Machines - Transaction Journal. KOMEL Institute ed.].
  • 41.Kulesz, B. & Sikora, A. Racjonalne przetwarzanie napięcia-czy budować układy 24-pulsowe? Maszyny Elektryczne: zeszyty problemowe. 2014. Vol. 2(102). P. 29-34. [In Polish: Rational energy transformation: 24-pulse circuit design? Electrical Machines - Transaction Journal, KOMEL Institute ed.].
  • 42.Kulesz, B. & Pasko, M. & Sikora, A. Trakcyjne wieloimpulsowe układy przetwarzania napięcia z dławikami sprzężonymi przy zasilaniu napięciem odkształconym. Prace Naukowe Politechniki Śląskiej. Elektryka. 2011. Vol. 2. P. 33-46. [In Polish: Multi-pulse traction energy transforming circuits with coupled reactors and distorted voltage supply. Scientific Journal of Silesian University of Technology, Elektryka].
  • 43.Hongping, Z. & Yan, L. & Junnian, W. A hybird active compensation device for current balance based on inductive filtering transformer. Transactions of China Electrotechnical Society. 2013. Vol. 28(8). P. 265-275.
  • 44.Zhu, H.P., et al. A hybrid active power compensation device for current balance of electrical railway system. In: Power System Technology (POWERCON), 2010 International Conference on. IEEE. 2010. P. 1-6.
  • 45.Toksari, M.D. Ant colony optimization for finding the global minimum. Applied Mathematics and Computation. 2006. Vol. 176(1). P. 308-316.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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