Comparison of Multi Agent Based PSO Approach for Optimal Power Flows with Security Constraints

Article Preview

Abstract:

This paper proposes a comparison of new evolutionary multi agent based particle swarm optimization algorithms for solving optimal power flows with security constraints (line flows and bus voltages). These methods combine the multi agents in two dimensional and cubic lattice structures with particle swarm optimization (PSO) to form two new algorithms. In both Two Dimensional Lattice Structured Multi Agent based Particle Swarm Optimization (TDLSMAPSO) and Cubic Lattice Structured Multi Agent based Particle Swarm Optimization (CLSMAPSO), an agent represents a particle in cubic lattice structure to PSO, and a candidate solution to the OPF problem. All agents live in a square and cubic lattice like environments, with agents fixed on a lattice point in the ascending order of their fitness value. In order to obtain the optimal solution, each agent in cubic and square lattice competes and cooperates with its neighbors. Making use of these agent-agent interactions, CLSMAPSO and TDLSMAPSO realizes the purpose of minimizing the objective function value. CLSMAPSO and TDLSMAPSO realizations were applied to IEEE 30 bus system. Simulation results show that proposed approaches gives better solution than earlier reported approaches in quick time.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

5206-5210

Citation:

Online since:

October 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] 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.

DOI: 10.1109/icpws.2009.5442741

Google Scholar

[2] 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.

DOI: 10.1109/wcica.2006.1713414

Google Scholar

[3] J. Kennedy, and R. Eberhart, Particle swarm optimization, Proc IEEE Int Conf Neural Networks, Aust, pp.1942-1948, (1995).

Google Scholar

[4] R.C. Eberhart, and Y. Shi, Comparing inertia weights and constriction factors in particle swarm optimization, Proc Congr Evolutionary Computation, pp.84-88, (2000).

DOI: 10.1109/cec.2000.870279

Google Scholar

[5] J. B. Park, K.S. Lee, J.R. Shin, and K.Y. Lee, A particle swarm optimization for economic dispatch with nonsmooth cost functions, IEEE Trans on Power Systems, pp.34-42, (2005).

DOI: 10.1109/tpwrs.2004.831275

Google Scholar

[6] 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).

DOI: 10.1016/j.ijepes.2006.01.001

Google Scholar

[7] M. Wooldridge, An introduction to multiagent system. New York: Wiley.

Google Scholar