Document Type : Research Paper


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


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.


Main Subjects

[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 research15(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 systems5(4), 1447-1454.
[4]      Granville, S. (1994). Optimal reactive dispatch through interior point methods. IEEE transactions on power systems9(1), 136-146.
[5]      Grudinin, N. (1998). Reactive power optimization using successive quadratic programming method. IEEE Transactions on power systems13(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 research6(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. Electrica19(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 computing53, 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 computing57, 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 systems12(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 journal20(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 technology60(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 management10(4), 801-810.
[15]   Basu, M. (2016). Quasi-oppositional differential evolution for optimal reactive power dispatch. International journal of electrical power & energy systems78, 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  
[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 technology15(8), 316-327.
[20]   Reddy, S. S. (2017). Optimal reactive power scheduling using cuckoo search algorithm. International journal of electrical & computer engineering7(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 systems54, 198-210.