Reduction of real power loss by white male deer mating based optimization algorithm

Reduction of real power loss by white male deer mating based optimization algorithm

Lenin Kanagasabai

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

Corresponding Author Email: 
gklenin@gmail.com
Page: 
61-68
|
DOI: 
https://doi.org/10.3166/ISI.23.5.61-68
Received: 
| |
Accepted: 
| | Citation

OPEN ACCESS

Abstract: 

In this paper, mating behaviour of the white male deer has been formulated to solve the reactive power problem. How the white male deers form the harem & how it mates with hinds are imitated to formulate the algorithm called as white male deer mating based optimization algorithm (WMDM). Proposed white male deer mating based optimization algorithm (WMDM) has been tested in standard IEEE 30 bus system. Simulation results show clearly the better performance of the proposed WMDM algorithm in reduction of real power loss.

Keywords: 

optimal reactive power, transmission loss, white deer, swarm optimization

1. Introduction
2. Problem formulation
3. White male deer mating based optimization algorithm
4. Simulation results
5. Conclusion
  References

Lee K. Y. (1984). Fuel-cost minimisation for both real and reactive-power dispatches. Proceedings Generation, Transmission and Distribution Conference, Vol. 131, No. 3, pp. 85-93. http://dx.doi.org/10.1049/ip-c:19840012

Deeb N. I. (1998). An efficient technique for reactive power dispatch using a revised linear programming approach. Electric Power System Research, Vol. 15, No. 2, pp. 121–134. http://dx.doi.org/10.1016/0378-7796(88)90016-8

Bjelogrlic M. R., Calovic M. S., Babic B. S. (1990). Application of Newton’s optimal power flow in voltage/reactive power control. IEEE Trans Power System, Vol. 5, No. 4, pp. 1447-1454. http://dx.doi.org/10.1109/59.99399

Granville S. (1994). Optimal reactive dispatch through interior point methods. IEEE Transactions on Power System, Vol. 9, No. 1, pp. 136–146. http://dx.doi.org/10.1109/59.317548

Grudinin N. (1998). Reactive power optimization using successive quadratic programming method. IEEE Transactions on Power System, Vol. 13, No. 4, pp. 1219–1225. http://dx.doi.org/10.1109/59.736232

Yan W., Yu J., Yu D. C., Bhattarai K. (2006). A new optimal reactive power flow model in rectangular form and its solution by predictor corrector primal dual interior point method. IEEE Trans. Pwr. Syst, Vol. 21, No. 1, pp. 61-67. http://dx.doi.org/10.1109/TPWRS.2005.861978

Mukherjee A., Mukherjee V. (2015). Solution of optimal reactive power dispatch by chaotic krill herd algorithm. IET Gener. Transm. Distrib, Vol. 9, No. 15, pp. 2351–2362. http://dx.doi.org/10.1049/iet-gtd.2015.0077

Hu Z., Wang X., Taylor G. (2010). Stochastic optimal reactive power dispatch: Formulation and solution method. Electr. Power Energy Syst, Vol. 32, pp. 615-621. http://dx.doi.org/10.1016/j.ijepes.2009.11.018

Morgan M. A. P., Abdullah N. R. H., Sulaiman M. H., Mustafa M., Samad R. (2016). Multi-Objective Evolutionary Programming (MOEP) using mutation based on Adaptive Mutation Operator (AMO) applied for optimal reactive power dispatch. ARPN Journal of Engineering and Applied Sciences, Vol. 11, No. 14.

Pandiarajan K., Babulal C. K. (2016). Fuzzy harmony search algorithm based optimal power flow for power system security enhancement. International Journal Electric Power Energy Syst, Vol. 78, pp. 72-79. http://dx.doi.org/10.1016/j.ijepes.2015.11.053

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 Adapter (AMO) and Polynomial Mutation Operator (PMO). Journal of Electrical Systems, pp. 12-1.

Mei R. N. S., Sulaiman M. H., Mustaffa Z. (2016). Ant Lion optimizer for optimal reactive power dispatch solution. Journal of Electrical Systems Special Issue AMPE2015, pp. 68-74.

Gagliano A., Nocera F. (2017). Analysis of the performances of electric energy storage in residential applications. International Journal of Heat and Technology, Vol. 35, No. 1, pp. S41-S48. http://dx.doi.org/10.18280/ijht.35Sp0106

Caldera M., Ungaro P., Cammarata G., Puglisi G. (2018). Survey-based analysis of the electrical energy demand in Italian households. Mathematical Modelling of Engineering Problems, Vol. 5, No. 3, pp. 217-224. http://dx.doi.org/10.18280/mmep.050313

Wu Q. H., Cao Y. J., Wen J. Y. (1998). Optimal reactive power dispatch using an adaptive genetic algorithm. Int. J. Elect.Power Energy Syst., Vol 20. pp. 563-569. http://dx.doi.org/10.1016/S0142-0615(98)00016-7

Zhao B., Guo C. X., Cao Y. J. (2005). Multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Trans. Power Syst., Vol. 20, No. 2, pp. 1070-1078. http://dx.doi.org/10.1109/TPWRS.2005.846064

Mahadevan K., Kannan P. S. (2010). Comprehensive learning particle swarm optimization for reactive power dispatch. Applied Soft Computing, Vol. 10, No. 2, pp. 641–652. http://dx.doi.org/10.1016/j.asoc.2009.08.038

Khazali A. H., Kalantar M. (2011). Optimal reactive power dispatch based on harmony search algorithm. Electrical Power and Energy Systems, Vol. 33, No. 3, pp. 684–692. http://dx.doi.org/10.1016/j.ijepes.2010.11.018

Sakthivel S. M., Manimozhi G. V. (2013). A nature inspired optimization algorithm for reactive power control in a power system. International Journal of Recent Technology and Engineering, Vol. 2, No. 1, pp. 29-33.

Sharma T., Srivastava L., Dixit S. (2016). Modified cuckoo search algorithm for optimal reactive power dispatch. Proceedings of 38 the IRF International Conference, pp. 4-8, 2.