Modified ABC Algorithm for Generator Maintenance Scheduling - ijcee

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International Journal of Computer and Electrical Engineering, Vol. 3, No. 6, December 2011 TABLE I: GMS SCHEDULE FOR CASE 1 WEEK NO GENERATING UNITS SCHEDULED FOR MAINTENANCE GENERATING UNITS SCHEDULED FOR MAINTENANCE WEEK NO DPSO MDPSO ABC MABC DPSO MDPSO ABC MABC 1 5 3,12 1 4 27 16 20 18 18,20 2 5 2,12 1 4 28 16,18 16 ---- ---- 3 5 2 1 4 29 16 16 14 16 4 4 6 1 5 30 16 16 14 16 5 4 6,9,13 1 5 31 16 16 14 16 6 4 6,9,13 1 5 32 16 16 14 16 7 3,6 6,13 1 1 33 --- 16 14 16 8 6,10,13 6,7 5 1 34 20 19 16 16 9 6,10,13 6,7,10 5 1,13 35 21 17,19 16 14 10 6,10,13 6,7,10 5 1,13 36 21 17 16 14 11 6,9,10 6,7,10 4 1,13 37 21 17 16 14 12 6,9 6,7,11 4 1,11 38 21 14 16 14 13 6 6,8,11 4 1,11 39 19 14 16 14 14 6,7 5 6,13 6,10 40 19 14 19 19 15 6,7 5 6,11,13 6,10 41 15 14 19,21 19,21 16 6,7 5 6,11,13 6,10 42 15 14 21 17,21 17 2,7 1 3,6 3,6,10 43 15 --- 21 17,21 18 2,12 1 6,9,10 6,9 44 15 15 17,21 17,21 19 8,12 1 6,9,10 6,9 45 15 15 17 --- 20 1 1 6,8,10 6,8 46 17 15 17,20 --- 21 1 1 6,10 6,7 47 17 15 15 15 22 1 1 6,7 6,7 48 14,17 15 15 15 23 1 1 6,7 6,7 49 14 21 15 15 24 1 4 2,7,12 2,7,12 50 14 21 15 15 25 1 4 2,7,12 2,12 51 14 21 15 15 26 1 4 --- --- 52 14 18,21 ---- ---- WEEK NO TABLE II: GMS SCHEDULE FOR CASE 2 GENERATING UNITS SCHEDULED FOR MAINTENANCE GENERATING UNITS SCHEDULED FOR MAINTENANCE WEEK MS - MS - MDPSO ABC MABC NO MDPSO ABC MABC MDPSO MDPSO 1 1 12,13 8,10 1,10,13 27 19 17,20 17,21 15 2 1 12,13 10 1,10,13 28 19,20 17,19 17,21 15 3 1 4,13 1,10 1,10,13 29 16 17,19 17,21 15 4 1 4 1,10 1,10 30 16 --- 21 15 5 1 4 1 1,11 31 16 14 19 15 6 1 2,6 1 1,11 32 16 14 19 --- 7 1,6 2,6 1 1 33 16 14 --- 16,18 8 3,6,11 6 1 --- 34 16 14 14 16 9 2,6,11 6 1 4 35 --- 14 14 16 10 2,6 6,7,8 2 2,4 36 17 --- 14 16 11 6 6,7 2,4 2,4 37 17 21 14 16 12 6 6,7 4 5,9 38 17 21 14 16 13 6,13 6,7 4,9 5,9 39 14 21 20 --- 14 6,10,13 6,7,11 5,9 5 40 14 21 --- --- 15 6,10,13 6,11 3,5 3,6 41 14 18 16 14,19 16 6,7,10 6 5,6 6 42 14 16 16 14,19 17 7,10 6 6 6 43 14 16 16 14 18 7,9,12 5,9 6 6 44 21 16 16,18 14 19 7,9,12 5,9 6,12,13 6,12 45 18,21 16 16 14 20 4 1 6,12,13 6,12 46 21 16 16 20,21 21 4 1 6,11,13 6,7 47 21 16 15 17,21 22 4 1,10 6,7,11 6,7 48 15 15 15 17,21 23 --- 1,10 6,7 6,7 49 15 15 15 17,21 24 5 1,10 6,7 6,7,8 50 15 15 15 --- 25 5 1,10 6,7 ---- 51 15 15 15 --- 26 5,8 1 --- ---- 52 15 15 --- --- 817
International Journal of Computer and Electrical Engineering, Vol. 3, No. 6, December 2011 WEEK NO TABLE III: GMS SCHEDULE FOR CASE 3 GENERATING UNITS SCHEDULED FOR MAINTENANCE DPSO MDPSO ABC MABC 1 4 4 2,6 1,10 2 4 4 2,6 1,10 3 4 4 6,3 1,10 4 6 6 6,7 1,10 5 6,7 2,6,13 6,7 1,11,13 6 6,7 2,6,13 6,7 1,11,13 7 2,6,7 6,13 6,7 1,13 8 2,6,7 3,6 6,13 4 9 3,6,13 6,12 6,13 4 10 6,13 6,11,12 6,13 4 11 6,10 6,10,11 11,12 5 12 6,10 6,10 11,12 5 13 6,10,11 6,7,10 ---- 5 14 10,11 7,10 4 ---- 15 12 7 4 2,6,7 16 12 7,8 4 2,6,7 17 5 5 5 6,7 18 5 5 5 6,7 19 5 5 5 6,8 20 1 1 1,8 6,9 21 1 1 1,9 6,9 22 1 1 1,9 6,12 23 1 1 1,10 6,12 24 1,8 1 1,10 6,3 25 1,9 1,9 1,10 --- 26 1,9 1,9 1,10 --- VIII. CONCLUSION The problem of generating optimal maintenance schedules of generating units for the purpose of maximizing economic benefits and improving reliable operation of a power system, subject to satisfying system constraints. In this paper, a Modified Artificial Bee Colony (MABC) algorithm is proposed to solve a challenging power system optimization problem of generating unit maintenance schedule. The MABC is a novel bio inspired algorithm suitable for engineering optimization problems, which is simple, robust and efficient in handling the constraints and produce better maintenance schedules. The modifications are made in the initialization stage and control parameter (limit) of the ABC algorithm, however these do not alter but improve the inherent search process of the algorithm. The performance of the proposed algorithm for solving GMS is tested with the 13 and 21 unit test system and the results are compared with earlier reported methods. It is evident from the comparison the Modified ABC provides a competitive performance in terms of optimal solution. It is also found that this method is to be a promising alternative approach for solving GMS problems in practical power system. ACKNOWLEDGMENT The authors gratefully acknowledge the management the support and facilities provided by the authorities of Annamalai University, Annamalainagar, India to carry out this research work. REFERENCES [1] A. J. Wood and B. F. Wollenberg, Power generation operation and control, New York: John Wiley and Sons, 1996. [2] R. Mukerji, H. M. Merrill, B. W. Erickson, and J. H. Parker, “Power plant maintenance scheduling: Optimizing economics and reliability,” IEEE Trans. Power Systems, Vol. 6, pp. 476-483, May 1991. [3] T. Satoh and K. Nara, “Maintenance scheduling by using simulated annealing method,” IEEE Trans. Power Systems, Vol. 6, pp. 850-857, May 1991. [4] Y. Wang and E. Handschin, “A new genetic algorithm for preventive unit maintenance scheduling of power systems,” Elect. Power Ener. Syst, Vol. 22, pp. 343-348, 2000. [5] A. Volkanovski, B. Mavko, T. Boševski, A. Čauševski, and M. Čepin, “Genetic algorithm optimization of the maintenance scheduling of generating units in a power system,” Reliab. Eng. Syst. Safety, Vol. 93, pp. 757-767, 2008. [6] I. El-Amin, S. Duffuaa, and M. Abbas, “A Tabu search algorithm for maintenance scheduling of generating units,” Elect. Power Syst. Res., Vol. 54, pp. 91- 99, 2000. [7] K. Y. Huang and H. T. Yang, “Effective algorithm for handling constraints in generator maintenance scheduling,” IEE Proc. Generation, Transmission and Distribution, Vol. 149, pp. 274-282, May 2002. [8] K. P. Dahal and N. Chakpitak, “Generator maintenance scheduling in power systems using metaheuristic-based hybrid approaches,” Elect. Power Syst. Res, Vol. 77, pp. 771-779, 2007. [9] S. P. Canto, “Application of Benders’ decomposition to power plant preventive maintenance scheduling,” Europ. J. Oper. Res. Vol. 184, pp. 759-777, 2008. [10] Y. Yare, G. K. Venayagamoorthy, and U. O. Aliyu, “Optimal generator maintenance scheduling using a modified discrete PSO,” IET Gener. Transm. Distrib. Vol. 2, pp. 834-846, May 2008. [11] Y. Yare and G. K. Venayagamoorthy, “Optimal-maintenance scheduling of generators using multiple swarms-MDPSO framework,” Engg. App. Artifi. Intelli, Vol. 23, pp. 895-910, 2010. [12] Yusuf Yare and Ganesh K. Venayagamoorthy, “Optimal scheduling of generator maintenance using modified discrete particle swarm optimization,” Proceedings of IEEE on iREP Symposium, August 19-24, Charleston, SC, USA, 2007. [13] D. Karaboga and B. Basturk, “On the performance of Artificial Bee Colony (ABC) Algorithm,” App. Soft Comput, Vol. 8, pp. 687-697, 2008. [14] Z. Changsheng, O. Dantong, and N. Jiaxu, “An artificial bee colony approach for clustering,” Expert Syst. with App, Vol. 37, pp. 4761-4767, 2010. [15] A. Bilal, “Chaotic bee colony algorithms for global numerical optimization,” Expert Syst. with App, Vol. 37, pp. 5682-5687, 2010. [16] S. L. Sabat, S. K. Udgata, and A. Abraham, “Artificial bee colony algorithm for small signal model parameter extraction of MESFET,” Engg. App. of Artifi. Intelli, Vol. 23, pp. 689-694, 2010. 818
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