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Optimal Power Flow Using Evolutionary Programming viz., PSO, CPSO, HDE
Abstract
This paper presents an approach to obtain the optimal load flow solution using three different intelligent techniques such as Particle Swarm Optimization (PSO), Crazy Particle Swarm Optimization (CPSO) and Hybrid Differential Evolution (HDE) subject to various system constraints. The above optimization techniques have a capability to provide global optimal solution in problem domains where a complete traversion of the whole search space is completely infeasible. The proposed method has been tested on Ward and Hale six bus system IEEE 14 bus test system and IEEE-30 bus test system. The solutions obtained are quite encouraging and useful in solving the optimal load flow problem. The algorithm and simulation are carried using Mat lab software.
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References
D. P. Kothari, I. J Nagrath, “Modern Power System Analysis”, Third edition, New York, 2007.
Olle l. Elgerd, “Electric energy systems theory”, Second Edition.
Hermann W. Dommel and William F. Tinney, “Optimal Power Flow Solutions”, IEEE Trans. on Power Apparatus Power Apparatus and Systems. , Vol. PAS-87 (10), 1968.
Ranjit Roy a, and S. P. Ghoshal, “A novel crazy swarm optimized economic load dispatch for various types of cost functions”, Int. Jour Electric Power and Energy Systems, 2007.
M. Saravanan, S. Mary Raja Slochanal, P. Venkatesh, J. Prince Stephen Abraham, “Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability”, Electric Power System Research, Vol. 77, No. 3-4, 2007, pp. 276-283.
Amgad A. EL-Dib, Hosam K. M. Youssef, M. M. EL-Metwally, Z. Osrnan, “Load flow solution using Hybrid Particle Swarm Optimization”, IEEE Trans. On Power Systems, 2004, pp. 742-746.
C. A. Roa-Sepulveda B. J. Pavez-Lazo “A Solution to the optimal power flow using simulated annealing”, IEEE Porto Power Tech Conference, 2001, 10-13 September, Portugal,
James Kennedy and Russell Eberhart, “Particle Swarm Optimization”, From Proc. IEEE Int'l. Conf. on Neural Networks (Perth, Australia), IEEE Service Center, Piscataway, NJ, IV: 1942-1948.
Granville S. Optimal Reactive Dispatch through Interior Point Method, IEEE Trans. Power System, 1994;9(1): 136–146.
Wu Y, Debs AS, Marsten RE. A direct nonlinear preditorcorrector Primal-dual interior point algorithm for optimal power flow. IEEE Trans Power Systems, 1994;9(21): 876–883.
Torres GL, Quintana VH. An interior point method for nonlinear optimal power flow using voltage rectangular coordinates, IEEE Trans Power System, 1998;13(4): 1211–1218. ook. Mill Valley, CA: University Science, 1989.
Prof. Dr. Radu Dogaru, “Architectures and Algorithms for Natural Computing”.
Yung - Chien Lin, Kao - Shing Hwang and Feng - Sheng Wang, “Co-Evolutionary Hybrid Differential Evolution for mixed-integer optimization problem”.
Carpentier JL. Contribution a l’e ´tude du Dispatching Economique. Bull-Soc Fr Elec Ser 1962;B3: 431–47.
Kuan-Yao Tsai and Feng-Sheng Wang Evolutionary optimization with data collocation for reverse engineering of biological networks
M. A. Pai Computer Technique in power system analysis
Jiang Yan, Hu Tiesong, Wu Xianing, A Modified Particle Swarm Optimization Algorithm
Samir SAYAH and Khaled ZEHAR, Using Evolutionary Computation to Solve the Economic Load Dispatch Problem
30 Bus sytem one line diagram , http: //www. ee. washington. edu/research/pstca/pf30/pg_tca30fig. htm
Stephen J. Chapman, MATLAB Programing for Engineers.
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