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A Novel TVA-REPSO Technique in Solving Generators Sizing Problems for South Sulawesi Network

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PL
Nowoczesna technika TVA-REPSO w rozwiązaniu zagadnienia doboru rozmiarów generatora w sieci elektroenergetycznej Południowej Sulawesi
Języki publikacji
EN
Abstrakty
EN
This paper present a novel optimization method, Time Varying Acceleration – Rank Evolutionary Particle Swarm Optimization (TVAREPSO) in solving optimum generator sizing for minimising power losses in the transmission system of South Sulawesi, Indonesia. A comparison between the proposed method and three other methods was done in order to find the best method to optimize the generators’ output size. The results show that the TVA-REPSO algorithm can obtain the same performance as PSO but it only required shorter computing time and can converges faster than the original PSO.
PL
W artykule przedstawiono matematyczną metodę rozwiązania zagadnienia znalezienia optymalnego rozmiaru generatora, w celu minimalizacji strat w elektroenergetycznym systemie przesyłowym Południowej Sulawesi w Indonezji. W algorytmie wykorzystano optymalizację roju cząstek ze zmiennym w czasie przyspieszeniem (ang. TVA-REPSO). Dokonano porównania z innymi metodami, pokazało, że opracowana metoda ma skuteczność podobną do klasycznej metody PSO, lecz krótszy czas obliczeń.
Rocznik
Strony
170--174
Opis fizyczny
Bibliogr. 22 poz., schem., tab.,
Twórcy
autor
  • Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bharu, Johor, Malaysia
  • Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bharu, Johor, Malaysia
autor
  • Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bharu, Johor, Malaysia
  • Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bharu, Johor, Malaysia
autor
  • Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
Bibliografia
  • [1] Martinez Ramos J.L., Quintana V.H., Transmission power loss reduction by interior-point methods implementation issues and practical experience, IEE Proc.-Gener. Transm. Distrib., 152 (2005 ), nr 1, 90-98.
  • [2] R.S., Narasimham S.V.L., Ramalingaraju M., Optimal capacitor placement in a radial distribution system using Plant Growth Simulation Algorithm, Electrical Power and Energy Systems 33,(2011),1133–1139.
  • [3] Abdullah N.R.H., Musirin I., Othman M.M.B.,Transmission loss minimization using evolutionary programming considering UPFC installation cost, International Review of Electrical Engineering (IREE), 5(2010) , nr 3, 1189 – 1203
  • [4] Mohammad K., Hossein S., Tohid B., Payam F., Noradin G., Solving Optimal Capacitor Allocation Problem using DE Algorithm in Practical Distribution Networks, Przegląd Elektrotechniczny, 88 (2012) nr (7a), pp. 90-93.
  • [5] Mishara S., Reddy G.D., Rao P.E. and Santosh K., Implementation of New Evolutionary Technique for Transmission Loss Reduction, IEEE Congress on Evolutionary Computation,(2007), 2331 - 2336
  • [6] Jangjit S., Kumkratug P., and Laohachai P., Reduction of Transmission line Loss by Using Interline Power Flow Controllers, IEEE Electrical Engineering/Electronics Computer Telecommunications and Information Technology, (2010).
  • [7] Lee S.J., Location of Superconducting Device in a Power Grid for System Loss Minimization using Loss Sensitivity , Sensitivity, IEEE Trans. On Applied Superconductivity, 17, (2007), nr 2, 2351-2354.
  • [8] Zhu J., Cheung K., Hwang D., Sadjadpour A., Operation Strategy for Improving Voltage Profile and Reducing System Loss, IEEE Transactions on Power Delivery, 25 (2010), nr 1, 390 – 397.
  • [9] Rahli M. , Pirotte P. , Optimal load flow using sequential unconstrained minimization technique (SUMT) method under power transmission losses minimization, Electric Power Systems Research, (1999) , nr 52 , 61–64.
  • [10] Esmin A.A., Torres G. L., Souza A.C.Z., A Hybrid Particle Swarm Optimization Applied to Loss Power Minimization, IEEE Transactions On Power Systems, 20 (2005) , nr 2, 859 – 862.
  • [11] Ramana N.V. and Chandrasekar K., Multi-Objective Genetic Algorithm to mitigate the Composite Problem of Total Transfe Capacity, Voltage Stability and Transmission Loss Minimization, North American Power Symposium, (2007), 644 – 649
  • [12] Montoya F.G., Ban R, Gil C., Esp A., Alcayde A., Gómez J, Minimization of voltage deviation and power losses in power networks using Pareto optimization methods, Engineering Applications of Artificial Intelligence ,(2010), nr 23, 695–703
  • [13] Bagriyanik F.G., Aygen Z. E., Bagriyanik M., Minimization of power transmission losses in series compensated systems using genetic algorithm, International Review of Electrical Engineering (IREE), 6 (2011), nr 2, 810 - 817.
  • [14] Kumar M.S., Renuga P., Application of bacterial foraging algorithm for enhancement of voltage stability using L-Index approach, International Review of Electrical Engineering, 6 (2010), nr 2, 922 – 928.
  • [15] de Souza B.A., and de Albuquerque J.M.C., Optimal Placement of Distributed Generators Networks Using Evolutionary Programming, IEEE CAPES (Brazilian Program for Graduate Personnel Improvement), (2006), 1 - 6
  • [16] Kamari N.A.M., Musirin I. and Othman M.M., Application of Evolutionary Programming in the Assessment of Dynamic Stability, The 4th International Power Engineering and Optimization Conference, (2010), 43 – 48
  • [17] John E. Hunt and Denise E. Cooke, Learning Using an Artificial Immune System, Journal of Network and Computer Application, Vol. 19, (1996), pp. 189 - 212
  • [18] Medzhitov R., Janeway J., Innate Immunity The Virtues of a Non-clone System of Recognition , Cell, (1997), nr 91, 295 - 298
  • [19] Hunt J., Timmis J., Cooke D., Neal M. and King C., The Development of an Artificial Immune System for Real World Applications, In Artificial Immune System and Their Applications, Springer-Verlag, (1998), 157-186.
  • [20] Milanovi J.V., Lu J., Application Of Artificial Immune System For Detecting Overloaded Lines And Voltage Collapse Prone Buses In Distribution Network, IEEE Bucharest Power Tech Conference, (2009), 1 – 7
  • [21] Kennedy J., Eberhart R.C., Particle Swarm Optimization, IEEE International Conference on Neural Networks IV, Piscataway, NJ 4, (1995) , 1942 – 1948
  • [22] S. Sumathi, P. Surekha, Computational Intelligence Paradigms Theory and Application Using Matlab, CRC Press Taylor And Perancis Group, (2010), 162 – 167.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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