Novel quantum-inspired firefly algorithm for optimal power quality monitor placement

Abstract

The application of a quantum-inspired firefly algorithm was introduced to obtain optimal power quality monitor placement in a power system. The conventional binary firefly algorithm was modified by using quantum principles to attain a faster convergence rate that can improve system performance and to avoid premature convergence. In the optimization process, a multi-objective function was used with the system observability constraint, which is determined via the topological monitor reach area concept. The multi-objective function comprises three functions: number of required monitors, monitor overlapping index, and sag severity index. The effectiveness of the proposed method was verified by applying the algorithm to an IEEE 118-bus transmission system and by comparing the algorithm with others of its kind.

Graphical abstract

Keywords

quantum-inspired binary firefly algorithm / topological monitor reach area / power quality

References

[1] Mohammadi M, Akbari Nasab M. Voltage sag mitigation with D-STATCOM in distribution systems. Australian Journal of Basic & Applied Sciences, 2011, 5(5): 201−207

[2] Cristaldi L, Ferrero A, Muscas C, Salicone S, Tinarelli R. The impact of Internet transmission on the uncertainty in the electric power quality estimation by means of a distributed measurement system. IEEE Transactions on Instrumentation and Measurement, 2003, 52(4): 1073−1078

[3] Eldery M A, El-Saadany F, Salama M M A. Optimum number and location of power quality monitors. In: 11th International Conference on Harmonics and Quality of Power. Lake Placid, USA, 2004, 50−57

[4] Olguin G, Vuinovich F, Bollen M H J. An optimal monitoring program for obtaining Voltage sag system indexes. IEEE Transactions on Power Systems, 2006, 21(1): 378−384

[5] Ibrahim A A, Mohamed A, Shareef H, Ghoshal S P. An effective power quality monitor placement method utilizing quantum-inspired particle swarm optimization. In: 2011 International Conference on Electrical Engineering and Informatics (ICEEI). Bandung, Indonesia, 2011, 1−6

[6] Almeida C F M, Kagan N. Allocation of power quality monitors by genetic algorithms and fuzzy sets theory. In: 15th International Conference on Intelligent System Applications to Power Systems. Curitiba, Brazil, 2009, 1−6

[7] Cebrian J C, Almeida C F M, Kagan N. Genetic algorithms applied for the optimal allocation of power quality monitors in distribution networks. In: 14th International Conference on Harmonics and Quality of Power (ICHQP). Bergamo, Italy, 2010, 1−10

[8] Ibrahim A A, Mohamed A, Shareef H, Ghoshal S P. Optimal placement of voltage sag monitors based on monitor reach area and sag severity index. In: IEEE Student Conference on Research and Development (SCOReD). Putrajaya, Malaysia, 2010, 467−470

[9] Elbeltagi E, Hegazy T, Grierson D. Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics, 2005, 19(1): 43−53

[10] Rashedi E, Nezamabadi-pour H, Saryazdi S. GSA: a gravitational search algorithm. Information Sciences, 2009, 179(13): 2232−2248

[11] Yang X. Nature-inspired Metaheuristic Algorithms. Luniver Press, 2008

[12] Chou Y H, Chiu C H, Yang Y J. Quantum-inspired tabu search algorithm for solving 0/1 knapsack problems. In: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation. Dublin, Ireland, 2011, 55−56

[13] Han K H, Kim J H. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transactions on Evolutionary Computation, 2002, 6(6): 580−593

[14] Ibrahim A A, Mohamed A, Shareef H. A novel quantum-inspired binary gravitational search algorithm in obtaining optimal power quality monitor placement. Journal of Applied Sciences, 2012, 12(9): 822−830

[15] Jeong Y W, Park J B, Jang S H, Lee K Y. A new quantum-inspired binary PSO: application to unit commitment problems for power systems. IEEE Transactions on Power Systems, 2010, 25(3): 1486−1495

[16] Sayadi M K, Ramezanian R, Ghaffari-Nasab N. A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. International Journal of Industrial Engineering Computations, 2010, 1(1): 1−10

[17] Moore M, Narayanan A. Quantum-inspired computing. 1995-11-20

[18] Hey T. Quantum computing: an introduction. Computing & Control Engineering Journal, 1999, 10(3): 105−112

[19] Ibrahim A A, Mohamed A, Shareef H, Ghoshal S P. A new approach for optimal power quality monitor placement in power system considering system topology. Przegląd Elektrotechniczny, 2012, 88: 272−276 (Electrical Review)

[20] Marler R T, Arora J S. The weighted sum method for multi-objective optimization: new insights. Structural and Multidisciplinary Optimization, 2010, 41: 853−862

[21] Christie R. Power system test case archive: 118 bus power flow test case. 1993-05

Acknowledgments

This work was carried out with the financial support from the University Kebangsaan Malaysia (Grant No. DIP-2012-30).

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg