Description and Examination of Optima Power Flow Using Traditional and Stochastic Optimization Techniques

Ragaleela D.*, Sivanagaraju S.**
* Senior Assistant Professor, Department of Electrical and Electronics Engineering, PVPSIT, Vijayawada, Andhra Pradesh, India.
** Professor, Department of Electrical and Electronics Engineering, JNTUK, Kakinada, Andhra Pradesh, India.
Periodicity:May - July'2018
DOI : https://doi.org/10.26634/jps.6.2.14934

Abstract

In modern power systems, optima power flow plays a crucial role in optimizing different types of objective functions for progress and outlining. The purpose of Optima Power Flow (OPF) problem is to attain the optima solution of a considered test system objective function, like fuel cost and loss minimization by fine tuning the power system control variables. In this paper a new stochastic hybrid method, Particle Movement Bee Colony Algorithm (PMBCA) based on Particle Swarm Optimization (PSO) and Honey Bee Colony (HBC) is proposed in addition to existing methods. Comparisons of various traditional and heuristic optimization methods are considered to solve the OPF problems. The utility and efficacy of suggested algorithms is exemplarily tested on the IEEE 30-bus test system. Results prove that the PMBCA algorithm gives better solution and convergence characteristics to enhance the system performance compared with other methods.

Keywords

Optima Power Flow (OPF), Particle Swarm Optimization (PSO), Honey Bee Colony (HBC), Particle Movement Bee Colony Algorithm (PMBCA).

How to Cite this Article?

Ragaleela, D and Sivanagaraju, S. (2018). Description and Examination Of Optima Power Flow Using Traditional and Stochastic Optimization Techniques. i-manager’s Journal on Power Systems Engineering, 6(2), 12-19. https://doi.org/10.26634/jps.6.2.14934

References

[1]. Abdullah, N. R. H., Musirin, I., Othman, M. M., & Rahman, T. K. A. (2009, February). Solving reactive power control problems in a stressed power system network using evolutionary computation technique. In Proceedings of the 8th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Databases (pp. 254-259). World Scientific and Engineering Academy and Society (WSEAS).
[2]. Asija, D., Astick, P. V., & Choudekar, P. (2018). Minimizing fuel cost of generators using GA-OPF. In Proceedings of First International Conference on Smart System, Innovations and Computing (pp. 331-339). Springer, Singapore.
[3]. Bhambu, P., Sharma, S., & Kumar, S. (2018). Modified Gbest Artificial Bee Colony Algorithm. In Soft Computing: Theories and Applications (pp. 665-677). Springer, Singapore.
[4]. Chansareewittaya, S., & Jirapong, P. (2015). Power transfer capability enhancement with multitype FACTS controllers using hybrid particle swarm optimization. Electrical Engineering, 97(2), 119-127.
[5]. Davis, L. (1991). Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold.
[6]. Del Valle, Y., Venayagamoorthy, G. K., Mohagheghi, S., Harley, R. G., & Hernandez, J. C. (2008). Particle swarm optimization: Basic concepts, variants and applications in power systems. IEEE Transactions on Evolutionary Computation, 12(2), 171-195.
[7]. Dinh, L., Vo Ngoc, D., & Vasant, P. (2013). Artificial bee colony algorithm for solving optimal power flow problem. The Scientific World Journal, 2013, 1-9.
[8]. Gao, W. F., Liu, S. Y., & Huang, L. L. (2014). Enhancing artificial bee colony algorithm using more informationbased search equations. Information Sciences, 270, 112- 133.
[9]. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, 1st Ed. Addison- Wesley Longman Publishing Co., Inc. Boston, MA, USA.
[10]. Haupt, R. L., & Haupt, S. E. (2004). Practical Genetic Algorithms, Second Edition. Wiley Publishers.
[11]. Holland, J. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan.
[12]. Kennedy, J., & Eberhart, R. C. (2001). Swarm Intelligence. San Francisco: Morgan Kaufmann.
[13]. Khamees, A., Badra, N., & Abdelaziz, A. (2016). Optimal power flow methods: A comprehensive survey. Int. Electr. Eng. J. (IEEJ), 7(4), 2228-2239.
[14]. Kothari, D. P., & Dhillon, J. S. (2011). Power System Optimization, Second Edition: PHI Publications.
[15]. Lee, K. Y., & El-Sharkawi, M. A. (Eds.). (2008). Modern Heuristic Optimization Techniques: Theor y and Applications to Power Systems (Vol. 39). John Wiley & Sons.
[16]. Li, Z., Wang, W., Yan, Y., & Li, Z. (2015). PS-ABC: A hybrid algorithm based on particle swarm and artificial bee colony for high dimensional optimization problems. Expert Systems with Applications, 42(22), 8881-8895.
[17]. Matos, L., Silva, D., & Soler, E. (2017, August). An analysis of the branch-and-bound method in solving the reactive optimal power flow problem. In Electronics, Electrical Engineering and Computing (INTERCON), 2017 IEEE XXIV International Conference on (pp. 1-4). IEEE.
[18]. Niknam, T., Narimani, M. R., Aghaei, J., & Azizipanah-Abarghooee, R. (2012). Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index. IET Generation, Transmission & Distribution, 6(6), 515-527.
[19]. Pedapenki, K. K., & Swathi, G. (2017). Application of Genetic Algorithm in Electrical Engineering. International Journal of Pure and Applied Mathematics, 114(8), 35-43.
[20]. Ramesh, G., & Kumar, T. K. S. (2015). Optimal dispatch of real power generation using classical methods. International Journal of Electronics and Electrical Engineering, 3(2), 115-120.
[21]. Shuqin, S., Bingren, Z., Jun, W., Nan, Y., & Qingyun, M. (2013, June). Power system reactive power optimization based on adaptive particle swarm optimization algorithm. In Digital Manufacturing and Automation (ICDMA), 2013 Fourth International Conference on (pp. 935-939). IEEE.
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