Self Adaptive Firefly Algorithm for Reducing both Power Losses and Net VSI in Power System

International Journal of Electrical and Electronics Engineering

© 2017 by SSRG - IJEEE Journal

Volume 4 Issue 8

Year of Publication : 2017

Authors : Dr. B. Suresh Babu

10.14445/23488379/IJEEE-V4I8P101

How to Cite?

Dr. B. Suresh Babu, "Self Adaptive Firefly Algorithm for Reducing both Power Losses and Net VSI in Power System," SSRG International Journal of Electrical and Electronics Engineering, vol. 4,  no. 8, pp. 1-6, 2017. Crossref, https://doi.org/10.14445/23488379/IJEEE-V4I8P101

Abstract:

Economic load dispatch (ELD) is an important operational problem is formulated as an optimization problem of minimizing the fuel cost while satisfying several equality and inequality constraints. Usually the network loss is calculated using constant B-loss coefficients and net VSI (voltage stability index) is calculated sum of VSI of all load buses. More accurate solution due to can be obtained, if network loss and net VSI is calculated from load flow. A method involving the self adaptive firefly algorithm for solving the ELD problem for AC system has been developed. The firefly algorithm (FA), a heuristic numeric optimization algorithm inspired by the behavior of fireflies, appears to be a robust and reliable technique. This paper presents a self adaptive firefly algorithm for reducing both power losses and net VSI in power system. The proposed algorithm (PA) is applied to the standard IEEE 30 bus test system and the result are presented to demonstrate its effectiveness

Keywords:

Economic Load Dispatch, Firefly Algorithm, Load Flow Analysis, Valve-Point Effects.

References:

1. Wood. AJ and Woolenberg. BF. (1996). Power generation, operation and control, John Willey and Sons, New York
2. Chowdhury.BH and Rahman. S. (1990). A review of recent advances in economic dispatch, IEEE Trans. on Power Systems, 5(4), 1248-1259.
3. Panigrahi. CK, Chattopadhyah. PK, Chakrabarti. RN and Basu. N. (2006). Simulated annealing technique for dynamic economic dispatch, Electric Power Components and Systems, 34(5), 577- 586.
4. Adhinarayanan. T and Sydulu. M. (2008). Diretional search genetic algorithm applications to economic dispatch of thermal units, International Journal for Computational Methods in Engineering Science and Mechanics, 9(4), 211-216.
5. He Da-Kuo, Wang Fu-li and Mao Zhi-zhong. (2008). Hybrid genetic algorithm for economic dispatch with value-point effect, Electric Power Systems Research, 78, 626-633.
6. JB. Park, KS. Lee, JR. Shin and KY.Lee. (2005). A particle swarm optimization for economic dispatch with nonsmooth cost function, IEEE Trans Power Syst, 20(1): 34-42.
7. Subbaraj. P, Rengaraj. R, Salivahanan. S and Senthilkumar. TR. (2010) Parallel particle swarm optimisation with modified stochastic acceleration factors for solving large scale economic dispatch problem, Electrical Power and Energy Systems, 32(9), 1014-1023.
8. A.Pereira-Neto, C.Unsihuay and OR.Saavedra. (2005). Efficient evolutionary strategy optimization procedure to solve the nonconvex economic dispatch problem with generator constraints. IEEE Proc Gener Transm Distrib., 152(5): 653-660.
9. X. S. Yang, Nature-Inspired Meta-Heuristic Algorithms, Luniver Press, Beckington, UK, 2008.
10. X. S. Yang. (2009). Firefly algorithms for multimodal optimization, in Proceedings of the Stochastic Algorithms: Foundations and Applications (SAGA ’09), Vol. 5792 of Lecture Notes in Computing Sciences, pp. 178–178, Springer, Sapporo, Japan.
11. Xin-She Yang. (2010) Firefly algorithm,stochastic test function and design optimisation ,International Journal of Bio-inspired computation Vol.2,No.2,pp 78-84.
12. Xin-She Yang.(2011) Review of metaheuristics and generalized evolutionary walk algorithm,International Journal of Bio-inspired computation Vol.3,No.2,pp 77-84.
13. Xin-She Yang ,(2013) Multiobjective firefly algorithm for continuous optimization,Engineering with computers,Vol.18,No.2,pp.175-184.
14. Kuldeep Kumar Swarnkar. (2012). Economic load dispatch problem with reduce power loss using firefly algorithm, Journal of Advanced Computer Science and Technology, 1(2): 42-56.
15. K. Vinod Kumar and G. Lakshmi Phani. (2011). Combined economic emission dispatch-pareto optimal front approach, International journal of computer applications, 30(12): 16-21.
16. M. H. Sulaiman, M. W. Mustafa, Z. N. Zakaria, O. Aliman and S. R. Abdul Rahim. Firefly Algorithm Technique for Solving Economic Dispatch Problem, 2012 IEEE International Power Engineering and Optimization Conference (PEOCO2012), Melaka, Malaysia: 6-7 June 2012.
17. K.Chandrasekaran and Sishaj P Simon. (2012). Tuned Fuzzy Adapted Firefly Lambda Algorithm for Solving Unit Commitment Problem, J. Electrical Systems,8(2): 132-150.
18. W.F. Tinney and C.E. Hart. (1967). Power flow solution by Newton’s method, IEEE Trans. PAS-86: 1449-1460.
19. TestSystems Archive, Available at http://www.ee.washington.edu/research/pstca/ (Accessed December 2012).
20. B.SureshBabu and A.Shunmugalatha Reducing Power Losses in Power System by Using Self Adaptive Firefly Algorithm (SEMCCO 2013) Part I ,LNCS 8297 , pp. 122-132,2013.Springer International Publishing Switzerland 2013.