Factual power loss reduction by augmented monkey optimization algorithm

Kanagasabai, L.

doi:10.22105/riej.2020.214459.1112

Document Type : Research Paper

Author

L. Kanagasabai

Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, India.

https://doi.org/10.22105/riej.2020.214459.1112

Abstract

This paper presents Augmented Monkey Optimization Algorithm (AMOA) applied to solve optimal reactive power problem. Communal behaviour of monkeys has been utilized to model the algorithm. Normally, group monkeys assess the distance from the source to food for foraging behaviour. Local leader renews its most excellent location inside the group, when the food source is not rationalized then the group will start probing in different directions for the food sources. Two most important control parameters are Global Leader Limit (GLlimit) and Local Leader Limit (LLlimit) which give appropriate way to global and local leaders correspondingly. Levy flight has been intermingled in the algorithm to enhance the search ability. Proposed AMOA accelerates the exploitation ability that has been tested in standard IEEE 14, 30, 57,118,300 bus test systems. The simulation results show the projected algorithm reduced the real power loss comprehensively.

Keywords

Main Subjects

Computational Intelligence

References

[1] Lee, K. Y., Park, Y. M., & Ortiz, J. L. (1984, May). Fuel-cost minimisation for both real-and reactive-power dispatches. In IEE proceedings C (generation, transmission and distribution), 131(3), 85-93.

[2] Deeb, N. I., & Shahidehpour, S. M. (1988). An efficient technique for reactive power dispatch using a revised linear programming approach. Electric power systems research, 15(2), 121-134.

[3] Bjelogrlic, M., Calovic, M. S., Ristanovic, P., & Babic, B. S. (1990). Application of newton's optimal power flow in voltage/reactive power control. IEEE transactions on power systems, 5(4), 1447-1454.

[4] Granville, S. (1994). Optimal reactive dispatch through interior point methods. IEEE transactions on power systems, 9(1), 136-146.

[5] Grudinin, N. (1998). Reactive power optimization using successive quadratic programming method. IEEE Transactions on power systems, 13(4), 1219-1225.

[6] Mahate, R. K., & Singh, H. (2019). Multi-objective optimal reactive power dispatch using differential evolution. International journal of engineering technologies and management research, 6(2), 27-38.

[7] Yalçın, E., Taplamacıoğlu, M. C., & Çam, E. (2019). The adaptive chaotic symbiotic organisms search algorithm proposal for optimal reactive power dispatch problem in power systems. Electrica, 19(1), 37-47.

[8] Naderi, E., Narimani, H., Fathi, M., & Narimani, M. R. (2017). A novel fuzzy adaptive configuration of particle swarm optimization to solve large-scale optimal reactive power dispatch. Applied soft computing, 53, 441-456.

[9] Heidari, A. A., Abbaspour, R. A., & Jordehi, A. R. (2017). Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems. Applied soft computing, 57, 657-671.

[10] Morgan, M., Abdullah, N. R. H., Sulaiman, M. H., Mustafa, M., & Samad, R. (2016). Benchmark studies on optimal reactive power dispatch (ORPD) based multi-objective evolutionary programming (MOEP) using mutation based on adaptive mutation operator (AMO) and polynomial mutation operator (PMO). Journal of electrical systems, 12(1), 121-132.

[11] Mouassa, S., Bouktir, T., & Salhi, A. (2017). Ant lion optimizer for solving optimal reactive power dispatch problem in power systems. Engineering science and technology, an international journal, 20(3), 885-895.

[12] Anbarasan, P., & Jayabarathi, T. (2017, April). Optimal reactive power dispatch problem solved by symbiotic organism search algorithm. 2017 innovations in power and advanced computing technologies (i-PACT) (pp. 1-8). Vellore, India: IEEE.

[13] Tighzert, L., Fonlupt, C., & Mendil, B. (2019). Towards compact swarm intelligence: a new compact firefly optimisation technique. International journal of computer applications in technology, 60(2), 108-123.

[14] Gosain, A., & Sachdeva, K. (2019). Selection of materialized views using stochastic ranking based Backtracking Search Optimization Algorithm. International journal of system assurance engineering and management, 10(4), 801-810.

[15] Basu, M. (2016). Quasi-oppositional differential evolution for optimal reactive power dispatch. International journal of electrical power & energy systems, 78, 29-40.

[16] Weise, T. (2009). Global optimization algorithms-theory and application. Thomas Weise.

[17] Bansal, J. C., Sharma, H., Jadon, H. S., & Clerc, M. Spider Monkey optimization algorithm for numerical optimization. Memetic Computing, 6, 31–47.

[18] IEEE 300 bus test. (1993). Retrieved October 01, 2019, from http://www.fglongatt.org/Test_Systems/IEEE_300bus.html

[19] Hussain, A. N., Abdullah, A. A., & Neda, O. M. (2018). Modified particle swarm optimization for solution of reactive power dispatch. Research journal of applied sciences, engineering and technology, 15(8), 316-327.

[20] Reddy, S. S. (2017). Optimal reactive power scheduling using cuckoo search algorithm. International journal of electrical & computer engineering, 7(5).

[21] Reddy, S. S., Bijwe, P. R., & Abhyankar, A. R. (2014). Faster evolutionary algorithm based optimal power flow using incremental variables. International journal of electrical power & energy systems, 54, 198-210.

International Journal of Research in Industrial Engineering

Factual power loss reduction by augmented monkey optimization algorithm

References

References

Volume 9, Issue 1
March 2020
Pages 1-12

Factual power loss reduction by augmented monkey optimization algorithm

References

References

Volume 9, Issue 1March 2020Pages 1-12

Volume 9, Issue 1
March 2020
Pages 1-12