Document Type
Article
Publication Date
1-1-2013
Abstract
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.
Keywords
Generators' optimal Output, Optimization Method, Power Loss Reduction, Voltage Stability Index.
Divisions
fac_eng
Publication Title
Przegląd Elektrotechniczny
Volume
89
Issue
2 A
Publisher
Nowoczesna technika TVA-REPSO w rozwiaogonekzaniu zagadnienia doboru rozmiarów generatora w sieci elektroenergetycznej Po�udniowej Sulawesi
Additional Information
Export Date: 17 April 2013 Source: Scopus Language of Original Document: English; Polish Correspondence Address: Jamian, J. J.; Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bharu, Johor, Malaysia; email: jasrul@fke.utm.my References: Martinez Ramos, J.L., Quintana, V.H., Transmission power loss reduction by interior-point methods implementation issues and practical experience (2005) IEE Proc.-Gener. Transm. Distrib., 152 (1), pp. 90-98; Narasimham, S.V.L., Ramalingaraju, M., Optimal capacitor placement in a radial distribution system using Plant Growth Simulation Algorithm (2011) Electrical Power and Energy Systems, 33, pp. 1133-1139; Abdullah, N.R.H., Musirin, I., Othman, M.M.B., Transmission loss minimization using evolutionary programming considering UPFC installation cost (2010) International Review of Electrical Engineering (IREE), 5 (3), pp. 1189-1203; Mohammad, K., Hossein, S., Tohid, B., Payam, F., Noradin, G., Solving Optimal Capacitor Allocation Problem using DE Algorithm in Practical Distribution Networks (2012) Przeglaogonekd Elektrotechniczny, 88 (7), pp. 90-93; Mishara, S., Reddy, G.D., Rao, P.E., Santosh, K., Implementation of New Evolutionary Technique for Transmission Loss Reduction (2007) IEEE Congress on Evolutionary Computation, pp. 2331-2336; Jangjit, S., Kumkratug, P., Laohachai, P., Reduction of Transmission line Loss by Using Interline Power Flow Controllers (2010) IEEE Electrical Engineering/Electronics Computer Telecommunications and Information Technology; Lee, S.J., Location of Superconducting Device in a Power Grid for System Loss Minimization using Loss Sensitivity (2007) IEEE Trans. On Applied Superconductivity, 17 (2), pp. 2351-2354. , Sensitivity; Zhu, J., Cheung, K., Hwang, D., Sadjadpour, A., Operation Strategy for Improving Voltage Profile and Reducing System Loss (2010) IEEE Transactions on Power Delivery, 25 (1), pp. 390-397; Rahli, M., Pirotte, P., Optimal load flow using sequential unconstrained minimization technique (SUMT) method under power transmission losses minimization (1999) Electric Power Systems Research, (52), pp. 61-64; Esmin, A.A., Torres, G.L., Souza, A.C.Z., A Hybrid Particle Swarm Optimization Applied to Loss Power Minimization (2005) IEEE Transactions On Power Systems, 20 (2), pp. 859-862; Ramana, N.V., Chandrasekar, K., Multi-Objective Genetic Algorithm to mitigate the Composite Problem of Total Transfe Capacity, Voltage Stability and Transmission Loss Minimization (2007) North American Power Symposium, pp. 644-649; 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 (2010) Engineering Applications of Artificial Intelligence, (23), pp. 695-703; Bagriyanik, F.G., Aygen, Z.E., Bagriyanik, M., Minimization of power transmission losses in series compensated systems using genetic algorithm (2011) International Review of Electrical Engineering (IREE), 6 (2), pp. 810-817; Kumar, M.S., Renuga, P., Application of bacterial foraging algorithm for enhancement of voltage stability using L-Index approach (2010) International Review of Electrical Engineering, 6 (2), pp. 922-928; de Souza, B.A., de Albuquerque, J.M.C., Optimal Placement of Distributed Generators Networks Using Evolutionary Programming (2006) IEEE CAPES (Brazilian Program for Graduate Personnel Improvement), pp. 1-6; Kamari, N.A.M., Musirin, I., Othman, M.M., Application of Evolutionary Programming in the Assessment of Dynamic Stability (2010) The 4th International Power Engineering and Optimization Conference, pp. 43-48; Hunt, J.E., Cooke, D.E., Learning Using an Artificial Immune System (1996) Journal of Network and Computer Application, 19, pp. 189-212; Medzhitov, R., Janeway, J., Innate Immunity The Virtues of a Non-clone System of Recognition (1997) Cell, (91), pp. 295-298; Hunt, J., Timmis, J., Cooke, D., Neal, M., King, C., The Development of an Artificial Immune System for Real World Applications (1998) In Artificial Immune System and Their Applications, pp. 157-186. , Springer-Verlag; Milanovi, J.V., Lu, J., Application Of Artificial Immune System For Detecting Overloaded Lines And Voltage Collapse Prone Buses In Distribution Network (2009) IEEE Bucharest Power Tech Conference, pp. 1-7; Kennedy, J., Eberhart, R.C., Particle Swarm Optimization (1995) IEEE International Conference on Neural Networks IV, 4, pp. 1942-1948. , Piscataway, NJ; Sumathi, S., Surekha, P., (2010) Computational Intelligence Paradigms Theory and Application Using Matlab, pp. 162-167. , CRC Press Taylor And Perancis Group