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A modified teaching-learning based optimization for the location and size of two SVCs to compensate the railway’s voltage drop

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PL
Metoda optymalizacji położenia i rozmiaru statycznego kompensatora mocy biernej SVCs w sieci trakcyjnej bazująca na algorytmach uczenia
Języki publikacji
EN
Abstrakty
EN
The recent concerns about fossil fuels have made mass transportations such as electric railways more popular than before. Meanwhile, traction loads are generally complex electrical loads that should be managed by the main electric grid when operated by the Railway Company. In this way, static VAr compensators (SVCs) is a precious tool for preserving the power quality of the electric grid in the presence of electric railways. Therefore, this paper discusses the locating of two SVCs in a rail way with modified teaching- learning based optimization (MTLBO). The results are compared with performing the optimization by Particle swarm optimization (PSO) algorithm.
PL
W artykule analizuje się metody optymalizacji położenia i rozmiaru statycznego kompensatora mocy biernej SVCs w sieci trakcyjnej. Do tego celu wykorzystuje się zmodyfikowaną metodę optymalizacji bazującej na algorytmie nauczania/uczenia się MTLBO.
Rocznik
Strony
41--45
Opis fizyczny
Bibliogr. 32 poz., rys., tab., wykr.
Twórcy
autor
  • School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
  • Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran
autor
  • Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
Bibliografia
  • [1] Bhargava B., Railway Electrification Systems and Configurations. IEEE Power Engineering Society Summer Meeting, (1999), 445-450
  • [2] Watanabe T., Trend of Railway Technologies and Power Semiconductor Devices, 11th International Symposium on Power Semiconductor Devices and Ics, (1999), 11-18
  • [3] Celli G., Pilo F., Tennakoon S.B., Voltage Regulation on 25 kV AC Railway Systems by Using Thyristor Switched Capacitor, Ninth International Conference on Harmonics and Quality of Power, (2000)
  • [4] Jianzong M., Mingli W., Shaobing Y., The Application of SVC for the Power Quality Control of Electric Railways. International Conference on Sustainable Power Generation and Supply, (2009)
  • [5] Qun-zhan L., A Study of Parallel Compensation Method in Railway Traction Power Supply Systems, Journal of Southwest Jiaotong University, 2 (1986)
  • [6] Kolar V., Kocman S., Filtration of harmonics in traction transformer substations, positive side effects on the additional harmonics, Przegląd Elektrotechniczny, 87 (2011), nr 12a, 44-46
  • [7] Xu Q., Zhu Q., Zhang H., Yuan X., A Rigorous Method for Power Quality Evaluation of High-speed Railway Using Electrical Transient Analyzer Program, Przegląd Elektrotechniczny, 88 (2012), nr 11a, 248-252
  • [8] Barnes R., Wong K.T., Unbalance and Harmonic Studies for the Channel Tunnel Railway System. IEE Proc.-B, I991. Vol. 138. No. 2.
  • [9] QUN-ZHAN L., ZHANG J., Qing-Quan Q., Optimization Design on Series Tuning Filtering and Reactive Compensation Used in Traction Systems. Int. Conf. on Main Line Railway Electrification, (1989), 222-226
  • [10] Tan P., Loh P., Holmes D., Morrison R., Application of Multilevel Active Power Filtering to a 25 kV Traction System, Australasian Universities Power Engineering Conf., (2002), 1-6
  • [11] Hu L., Morrison R.E., Young D.J., Comparison of Physical and Digital Modeling Techniques for a Compensated Railway System. Advances in Engineering Software, 19 (1994), 61-67.
  • [12] Kulworaswanichpong T., Goodman C.J., Optimal Area Control of AC Railway Systems via PWM Traction Drives. IEE Proc. Electric Power Applications, 152 (2005), no. 1, 33-40
  • [13] Tan P.C., Morrison R.E., Holmes D.G., Voltage Form Factor Control and Reactive Power Compensation in a 25kv Electrified Railway System Using a Shunt Active Filter Based on Voltage Detection. 4th IEEE Int. Conf. on Power Electronics and Drive Systems, 2001.
  • [14] Xu X., Chen B., Gan F., Electrical Railway Active Power Filter Research Based on Genetic Algorithms. IEEE Int. Conf. on Control and Automation, 2007.
  • [15] rahmani S., Alhadad K., A Single Phase Multilevel Hybrid Power Filter for Electrified Railway Applications. IEEE Int. Symp. on Industrial Electronics, 2006.
  • [16] Samet H., Mojallal A., Ghanbari T., Employing Grey System Model for Prediction of Electric Arc Furnace Reactive Power to Improve Compensator Performance, Przegląd Elektrotechniczny, 89 (2013), nr 12, 110-115
  • [17] Morrison R.E., Warbortun K., Young D.J., The application of shunt compensation on AC railways, IEE Int. Conf. on Electric Railway Systems for a new century, (1987)
  • [18] Morrison R.E., Warbortun K., Hackwell D., The Use of Static Shunt Compensation to Upgrade Existing Electrified Railways. IEE Int. Conf. on main line railway electrification, (1989)
  • [19] Kavousi-Fard A., Akbari-Zadeh M.R., Reliability Enhancement Using Optimal Distribution Feeder Reconfiguration, Neurocomputing, 106 (2013), 1–11
  • [20] Kavousi-Fard A., Samet H., Multi-objective Performance Management of the Capacitor Allocation Problem in Distributed System Based on Modified HBMO Evolutionary Algorithm, Electric Power and Component systems, 41 (2013), no 13, 1223-1247
  • [21] Baziar A., Kavousi-Fard A., Consideration Effect of Uncertainty in the Optimal Energy Management of Renewable Micro-Grids including Storage Devices, Renewable Energy, 59 (2013), 158-166
  • [22] Eslami M., Shareef H., Mohamed A., Khajehzadeh M., Particle Swarm Optimization for Simultaneous Tuning of Static Var Compensator and Power System Stabilizer, Przegląd Elektrotechniczny, 87 (2011), nr 9a, 343-347
  • [23] Kavousi-Fard A., Niknam T., Optimal Distribution Feeder Reconfiguration for Reliability Improvement Considering Uncertainty, IEEE Trans. on Power Delivery, 29 (2014), no 3, 1344-1353
  • [24] Kavousi-Fard A., Samet H., Marzban F., A New Hybrid Modified Firefly Algorithm and Support Vector Regression Model for Accurate Short Term Load Forecasting, Expert Systems With Applications, 41(2014), no 13, 6047–6056
  • [25] Kavousi-Fard A., Niknam T., Golmaryami M., Short Term Load Forecasting of Distribution Systems by a New Hybrid Modified FA-Backpropagation Method, Journal of Intelligent and Fuzzy systems, 26 (2014), 517-522
  • [26] Rao R.V., Savsani V.J., Vakharia D.P., Teaching–learningbased optimization: A novel method for constrained mechanical design optimization problems, Computer-Aided Design, 43 (2011), 303-315
  • [27] Niknam T., Kavousi-Fard A., Baziar A., Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants, Energy, 4(2012), no. 1, 563–573
  • [28] Kavousi-Fard A., A new fuzzy-based feature selection and hybrid TLA–ANN modeling for short-term load forecasting, Journal of Experimental & Theoretical Artificial Intelligence, 25 (2013), no.4, 543-557
  • [29] Millonas M.M., Swarms, Phase Transitions, and Collective Intelligence. In C. G. Langton. Artificial Life III. Addison Wesley. Reading, MA. 1994.
  • [30] Kenned J., Eberhart R., Particle Swarm Optimization. Proc. IEEE Int. Conf. on Neural Networks, 1995.
  • [31] Kenned J., Eberhart R., Swarm Intelligence, Morgan Kaufmann Publishers, Inc. Sanfrancisco, CA, 2001.
  • [32] Clerc M., The Swarm and the Queen: Towards a determininistic and adaptive particle swarm optimization. In Congress on Evolutionary Computation (CEC99), 1999,1951-1957
Typ dokumentu
Bibliografia
Identyfikator YADDA
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