Document Type : Research Paper


Department of EEE Prasad V.Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh-520007, India.


Acridoidea Stirred Artificial Bee Colony (ASA) Algorithm is applied to solve the power loss reduction problem. In the projected algorithm natural Acridoidea jumping phenomenon has been imitated and the modeled design has been intermingled with artificial bee colony algorithm. In the proposed algorithm, the position update has been done through the distance of jumping done by Acridoidea. The distance (D) is horizontal (h) with angle (θ), velocity (V) parameter amplifying the rate which is based on the gravity of Ballistic projectile. Normally, the angle will be 45◦ horizontal and it depends on the take-off velocity. ASA algorithm has been tested in standard IEEE 57 bus test system and results show that the proposed ASA algorithm reduced the real power loss effectively.


Main Subjects

[1]      Lenin, K. (2019). Acridoidea stimulated artificial bee colony algorithm for solving optimal reactive power problem. 5th virtual international conference on science, technology and management in energy (pp. 28-29).
[2]      Lee, K. Y. (1984). Fuel-cost minimisation for both real and reactive-power dispatches. Proceedings generation, transmission and distribution conference (pp. 85-93). 10.1049/ip-c.1984.0012
[3]      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.
[4]      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. 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]      Roy, P. K., & Dutta, S. (2019). Economic load dispatch: optimal power flow and optimal reactive power dispatch concept (pp. 46-64). In Optimal power flow using evolutionary algorithms. IGI Global.
[7]      Bingane, C., Anjos, M. F., & Le Digabel, S. (2019). Tight-and-cheap conic relaxation for the optimal reactive power dispatch problem. IEEE transactions on power systems34(6), 4684-4693.
[8]      Prasad, D., & Mukherjee, V. (2018). Solution of optimal reactive power dispatch by symbiotic organism search algorithm incorporating FACTS devices. IETE journal of research64(1), 149-160.
[9]      Aljohani, T. M., Ebrahim, A. F., & Mohammed, O. (2019). Single and multiobjective optimal reactive power dispatch based on hybrid artificial physics–particle swarm optimization. Energies12(12), 2333.
[10]  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.
[11]  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.
[12]  Mouassa, S., & Bouktir, T. (2019). Multi-objective ant lion optimization algorithm to solve large-scale multi-objective optimal reactive power dispatch problem. COMPEL-the international journal for computation and mathematics in electrical and electronic engineering, 38(1), 304-324.
[13]  Aljohani, T. M., Ebrahim, A. F., & Mohammed, O. (2019). Single and multiobjective optimal reactive power dispatch based on hybrid artificial physics–particle swarm optimization. Energies12(12), 2333.
[14]  Chen, G., Liu, L., Zhang, Z., & Huang, S. (2017). Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints. Applied soft computing50, 58-70.
[15]  Karaboga, D., & Akay, B. (2011). A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Applied soft computing11(3), 3021-3031.
[16]  The IEEE-test systems. (1993). Retrieved from
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.