Real Power Loss Reduction by Amplified Water Cycle Algorithm

  • Kanagasabai Lenin Prasad V. Potluri Siddhartha Institute of Technology, India (IN)


In this paper Amplified Water Cycle Algorithm (AWCA) has been used to solve the optimal reactive power problem. Water cycle algorithm (WCA) is a methodology which inspired by the hydrological cycle which happen in nature. In this work water cycle algorithm hybridized with Gravitational Search Algorithm, Chaos theory. In the projected Amplified Water Cycle Algorithm (AWCA) - with reference to the fitness value, population is first alienated into three groups: streams, rivers and sea. Through this hybridization exploration and exploitation is effectively improved. Positions of particles are initially modernized according to gravitational search.  Chaos theory is then defined and integrated in water cycle algorithm to modernize the population which will augment explore capability and population diversity. Projected Amplified Water Cycle Algorithm (AWCA) has been tested in standard IEEE 14, 30, 57, 300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.


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K. Y. Lee.(1984). “Fuel-cost minimisation for both real and reactive-power dispatches,” Proceedings Generation, Transmission and Distribution Conference, vol/issue: 131(3), pp. 85-93.

N. I. Deeb.(1998). “An efficient technique for reactive power dispatch using a revised linear programming approach,” Electric Power System Research, vol/issue: 15(2), pp. 121–134.

M. R. Bjelogrlic, M. S. Calovic, B. S. Babic. (1990). ”Application of Newton’s optimal power flow in voltage/reactive power control”, IEEE Trans Power System, vol. 5, no. 4, pp. 1447-1454.

S. Granville.(1994). “Optimal reactive dispatch through interior point methods,” IEEE Transactions on Power System, vol/issue: 9(1), pp. 136–146.

N. Grudinin.(1998). “Reactive power optimization using successive quadratic programming method,” IEEE Transactions on Power System, vol/issue: 13(4), pp. 1219–1225.

Wei Yan, J. Yu, D. C. Yu , K. Bhattarai.(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.

Aparajita Mukherjee, Vivekananda Mukherjee, (2015). “Solution of optimal reactive power dispatch by chaotic krill herd algorithm", IET Gener. Transm. Distrib, , Vol. 9, Issue. 15, pp. 2351–2362.

Hu, Z., Wang, X. & Taylor.(2010). “Stochastic optimal reactive power dispatch: Formulation and solution method”. Electr. Power Energy Syst., vol. 32, pp. 615-621.

Mahaletchumi A/P Morgan, Nor Rul Hasma Abdullah, Mohd Herwan Sulaiman,Mahfuzah Mustafa and Rosdiyana Samad.(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.

Mahaletchumi Morgan, Nor Rul Hasma Abdullah, Mohd Herwan Sulaiman, Mahfuzah Mustafa, Rosdiyana Samad.(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, 12-1.

Rebecca Ng Shin Mei, Mohd Herwan Sulaiman, Zuriani Mustaffa,. (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, Special Issue 1, pp. S41-S48. DOI: 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. DOI: 10.18280/mmep.050313

E. Rashedi, S. Nezamabadi, and S. Saryazdi, "GSA: A Gravitational Search Algorithm, "Information Sciences, vol. 179, no. 13, pp. 2232-2248, 2009.

O. Abdel-Raouf, I. El-henawy and M. Abdel-Baset "chaotic Harmony Search Algorithm with Different Chaotic Maps for Solving Assignment Problems "International Journal of Computational Engineering & Management, Vol. 17, pp. 10-15 ,2014.

Hadi Eskandar , Ali Sadollah , Ardeshir Bahreininejad , Mohd Hamdi, (2012) , “Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems”, Computers and Structures, 110-111, p.151-166, November, doi>10.1016/j.compstruc.2012.07.010.

IEEE, “The IEEE-test systems”, (1993),

Subbaraj, P. and P.N. Rajnarayan, 2009. Optimal reactive power dispatch using self-adaptive real coded Genetic algorithm. Electr. Power Syst. Res., 79(2): 374-38.

Pandya, S. and R. Roy, 2015. Particle swarm optimization based optimal reactive power dispatch. Proceeding of the IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp: 1-5.

Ali Nasser Hussain, Ali Abdulabbas Abdullah and Omar Muhammed Neda, (2018),“Modified Particle Swarm Optimization for Solution of Reactive Power Dispatch”, Research Journal of Applied Sciences, Engineering and Technology 15(8): 316-327, 2018 DOI:10.19026/rjaset.15.5917.

S. Surender Reddy, “Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm”, International Journal of Electrical and Computer Engineering, Vol. 7, No. 5, pp. 2349-2356. 2017.

S.S. Reddy, et al., “Faster evolutionary algorithm based optimal power flow using incremental variables”, Electrical Power and Energy Systems, vol. 54, pp. 198-210, 2014.

Received 2020-01-28
Accepted 2020-05-01
Published 2020-05-09
How to Cite
K. Lenin, “Real Power Loss Reduction by Amplified Water Cycle Algorithm”, J. Appl. Sci. Eng. Technol. Educ., vol. 2, no. 1, pp. 79-87, May 2020.