Application of imperialist competitive optimization algorithm in power industry

Abstract: In future electricity industry transferring high quality of power is essential. In this case, using Flexible AC Transmission System (FACTS) devices is inevitable. FACTS devices are used for controlling the voltage, stability, power flow and security of transmission lines. Therefore, finding the optimal locations for these devices in power networks is necessary. There are several varieties of FACTS devices with different characteristics, deployed for different purposes. Imperialist Competitive (IC) algorithm is a recently developed optimization technique, applied in power systems. IC algorithm is a new heuristic approach for global optimization searches based on the concept of imperialistic competition. In this paper, an IEEE 4-bus system is deployed as a case study in order to demonstrate the results of this novel approach using MATLAB.

How to cite this paper

 Alikhanzadeh, A & Shahrazad, M. (2015). Application of imperialist competitive optimization algorithm in power industry.International Journal of Industrial Engineering Computations , 5(1), 43-58.

Refrences

Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE Congress o Evolutionary Computation, 2007. CEC 2007. (pp. 4661-4667).

Avramenko, V. N. (2005, June). Power system stability assessment for current states of the system. In Power Tech, 2005 IEEE Russia (pp. 1-6). IEEE.

Belsnes, M. M., Warland, G., & Wolfgang, O. (2011, June). Simulating equilibrium prices in oligopoly power markets. IEEE Trondheim in PowerTech, 2011 (pp. 1-8). IEEE.

Cai, L. J., Erlich, I., & Stamtsis, G. (2004, October). Optimal choice and allocation of FACTS devices in deregulated electricity market using genetic algorithms. In Power Systems Conference and Exposition, 2004. IEEE PES(pp. 201-207). IEEE.

Chansareewittaya, S., & Jirapong, P. (2010, November). Power transfer capability enhancement with multitype FACTS controllers using particle swarm optimization. In TENCON 2010-2010 IEEE Region 10 Conference (pp. 42-47).

Deep, K., & Bansal, J. C. (2009, December). Optimization of directional overcurrent relay times using Laplace Crossover Particle Swarm Optimization (LXPSO). In Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on (pp. 288-293).

Gerbex, S., Cherkaoui, R., & Germond, A. J. (2001). Optimal location of multi-type FACTS devices in a power system by means of genetic algorithms. Power Systems, IEEE Transactions on, 16(3), 537-544.

Hong-Bo, S., & Shuxia, L. (2008, October). The Comparison Between Genetic Simulated Annealing Algorithm and Ant Colony Optimization Algorithm for ASP. In Wireless Communications, Networking and Mobile Computing, WiCOM & apos; 08. 4th International Conference on (pp. 1-6). IEEE.

Krishans, Z., Kutjuns, A., & Kalnins, M. (2007, July). Method of transmission power networks reliability estimation. In Power Tech, 2007 IEEE Lausanne (pp. 1996-1999). IEEE.

Majumdar, S., Chakraborty, A. K., & Chattopadhyay, P. K. (2009, December). Active power loss minimization with FACTS devices using SA/PSO techniques. International Conference on Power Systems, 2009. ICPS & apos; 09. (pp. 1-5). IEEE.

Minasian, A. A., & Bird, T. S. (2013). Particle Swarm Optimization of Microstrip Antennas for Wireless Communication Systems. IEEE Transactions on Antennas and Propagation, 61(12), 6214-6217.

Panda, S., & Padhy, N. P. (2008). Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design. Applied Soft Computing, 8(4), 1418-1427.

Rajabi-Ghahnavieh, A., Fotuhi-Firuzabad, M., Shahidehpour, M., & Feuillet, R. (2009). Optimal allocation of available transfer capability in operating horizon. IEEE Transactions on Power Systems, 24(2), 967-975.

Rajabioun, R., Hashemzadeh, F., Atashpaz-Gargari, E., Mesgari, B., & Salmasi, F. R. (2008, July). Identification of a MIMO evaporator and its decentralized PID controller tuning using colonial competitive algorithm. In be presented in IFAC World Congress.

Saravanan, M., Slochanal, S., Venkatesh, P., & Abraham, J. (2007). Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability. Electric Power Systems Research, 77(3), 276-283.

Shahrazad, M., & Zobaa, A. F. (2013). Optimal allocation of FACTS devices in distribution networks using Imperialist Competitive Algorithm. International Review of Electrical Engineering, 8(5).

Tabatabaei, N. M., Aghajani, G., Boushehri, N. S., & Shoarinejad, S. (2011). Optimal location of FACTS devices using adaptive particle swarm optimization mixed with simulated annealing. International Journal on Technical and Physical Problems of Engineering (IJTPE), (7), 60-70.

Takasaki, M. (2006, May). Power transfer capability enhancement with UPFC under circumstances of uncertain power flow pattern. In Transmission and Distribution Conference and Exhibition, 2005/2006 IEEE PES (pp. 659-665). IEEE.