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A Novel Multi agent based PSO approaches for security Constrained Optimal Power Flows using smooth and non-smooth cost functions
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 41 - Number 3 |
| Year of Publication: 2012 |
| Authors: K. Ravi Kumar, S. Anand, M. Sydulu |
10.5120/5521-7552
|
K. Ravi Kumar, S. Anand, M. Sydulu . A Novel Multi agent based PSO approaches for security Constrained Optimal Power Flows using smooth and non-smooth cost functions. International Journal of Computer Applications. 41, 3 ( March 2012), 14-21. DOI=10.5120/5521-7552
Abstract
This paper puts forward two new evolutionary multi agent based particle swarm optimization algorithms for solving security constrained (line flows and bus voltages) optimal power flows. These two methods combine the multi agents in two dimensional and cubic lattice structures with particle swarm optimization (PSO). All agents occupy in a cubic and square lattice like environments, with agents fixed on a lattice point in the ascending order of their fitness values. To obtain the optimal solution, each agent in cubic and square lattice competes and cooperates with its neighbor. Making use of these agent-agent interactions, CLSMAPSO and TDLSMAPSO accomplish the purpose of minimizing the Fuel cost value while maintaining all the constraints. In this paper, a Variable constriction factor has been considered for TDLSMAPSO and CLSMAPSO. Both the smooth and non-smooth cost functions were considered to take the effect of multiple fuels and multiple valves effects in to consideration. The outcomes are compared with many other methods like Genetic Algorithms, Differential Evolution, Normal PSO and Ant Colony optimization etc. , The OPF problem has been considered with three different cost functions to realize Optimal Power Flow using CLSMAPSO and TDLSMAPSO applied to IEEE 30 bus system. This unique method has the advantage of more agent interactions in case of CLSMAPSO which improved the convergence drastically compared to the Two dimensional structure used in paper by sivasubramani and Shanti swarup in reference[2]. It is found that the proposed method is found to be computationally fast, robust, superior and promising from its convergence characteristics.
References
- Hermann W. Dommel, and William F. Tinney,"optimal Power Flow Solutions"
- Sivasubramani. S, Shanti Swarup K, Multiagent Based Particle Swarm Optimization Approach to Economic Dispatch With Security Constraints December27-29'2009, International Conference on Power systems, ,Kharagpur" .
- Bo Yang,Yuping ChenmZunlian Zhao and Qiye Han, "Solving Optimal Power Flow Problems with improved Particle Swarm Optimization",Proceedings of 6th world congress on Intelligent Control and Automation,June 21-23,2006,Dalian,China.
- J. Nanda, D. P. Kothari, and S. C. Srivastava, "New optimal power dispatch using fletcher's quadratic programming method," IEEE Proc C, pp. 153-161, 1989.
- J. Nanda, and B. R. Narayanan, "Application of genetic algorithm to economic load dispatch with line flow constraints," Int Journal of Electric Power Energy Systems, pp. 723-729, 2002.
- P. Somasundaram, and K. Kuppusamy, "Application of evolutionary programming to security constrained economic dispatch," Electric Power and Energy Systems, pp. 343-351, 2005.
- J. Kennedy, and R. Eberhart, "Particle swarm optimization," Proc IEEE Int Conf Neural Networks, Aust, pp. 1942-1948, 1995.
- R. C. Eberhart, and Y. Shi, "Comparing inertia weights and constriction factors in particle swarm optimization," Proc Congr Evolutionary Computation, pp. 84-88, 2000.
- J. B. Park, K. S. Lee, J. R. Shin, and K. Y. Lee, "A particle swarm optimization for economic dispatch with non-smooth cost functions," IEEE Trans on Power Systems, pp. 34-42, 2005.
- K. S. Swarup, and P. Rohit Kumar, "A new evolutionary computation technique for economic dispatch with security constraints," Electric Power and Energy Systems, pp. 273-283, 2006.
- M. Wooldridge, An introduction to multiagent system. New York: Wiley, 2002.
- W. Zhong, J. Liu, M. Xue, and L. Jiao, " A multiagent genetic algorithm for global numerical optimization," IEEE Trans on Sys, Man and Cybernatics, pp. 1128-1141, 2004.
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