• Dr.K.Lenin Professor Prasad V. Potluri Siddhartha Institute of Technology Kanuru, Vijayawada, Andhra Pradesh -520007, India




Reactive Power, Transmission Loss, Enhanced Seeker, Optimization

Abstract [English]

This paper projects Enhanced Seeker Optimization (ESO) algorithm for solving optimal reactive power problem. Seeker optimization algorithm (SOA) models the deeds of human search population based on their memory, experience, uncertainty reasoning and communication with each other. In Artificial Bee Colony (ABC) algorithm the colony consists of three groups of bees: employed bees, onlookers and scouts. All bees that are presently exploiting a food source are known as employed bees. The number of the employed bees is equal to the number of food sources and an employed bee is allocated to one of the sources. In this paper hybridization of the seeker optimization algorithm with artificial bee colony (ABC) algorithm has been done to solve the optimal reactive power problem. Enhanced Seeker Optimization (ESO) algorithm combines two different solution exploration equations of the ABC algorithm and solution exploration equation of the SOA in order to progress the performance of SOA and ABC algorithms.  At certain period’s seeker’s location are modified by search principles obtained from the ABC algorithm, also it adjust the inter-subpopulation learning phase by using the binomial crossover operator. In order to evaluate the efficiency of proposed Enhanced Seeker Optimization (ESO) algorithm it has been tested in standard IEEE 57,118 bus systems and compared to other specified algorithms. Simulation results clearly indicate the best performance of the proposed Enhanced Seeker Optimization (ESO) algorithm in reducing the real power loss and voltage profiles are within the limits.


Download data is not yet available.


O.Alsac, B. Scott, “Optimal load flow with steady state security”,IEEE Transaction. PAS -1973, pp. 745-751. DOI: https://doi.org/10.1109/TPAS.1974.293972

Lee K Y ,Paru Y M , Oritz J L –A united approach to optimal real and reactive power dispatch , IEEE Transactions on power Apparatus and systems 1985: PAS-104 : 1147-1153 DOI: https://doi.org/10.1109/TPAS.1985.323466

A.Monticelli , M .V.F Pereira ,and S. Granville , “Security constrained optimal power flow with post contingency corrective rescheduling” , IEEE Transactions on Power Systems :PWRS-2, No. 1, pp.175-182.,1987. DOI: https://doi.org/10.1109/TPWRS.1987.4335095

Deeb N, ShahidehpurS.M, Linear reactive power optimization in a large power network using the decomposition approach. IEEE Transactions on power system 1990: 5(2) : 428-435 DOI: https://doi.org/10.1109/59.54549

E. Hobson ,’Network consrained reactive power control using linear programming, ‘ IEEE Transactions on power systems PAS -99 (4) ,pp 868-877, 1980 DOI: https://doi.org/10.1109/TPAS.1980.319715

K.Y Lee, Y.MPark, and J.L Oritz, “Fuel –cost optimization for both real and reactive power dispatches”, IEE Proc; 131C, (3), pp.85-93. DOI: https://doi.org/10.1049/ip-c.1984.0012

M.K. Mangoli, and K.Y. Lee, “Optimal real and reactive power control using linear programming” , Electr.Power Syst.Res, Vol.26, pp.1-10,1993. DOI: https://doi.org/10.1016/0378-7796(93)90063-K

Berizzi.C.Bovo,M.Merlo,andM.Delfanti,(2012), “A GA approach to compare ORPF objective functions including secondary voltage regulation,” Electric Power Systems Research, vol. 84, no. 1, pp. 187 – 194. DOI: https://doi.org/10.1016/j.epsr.2011.11.014

D. Devaraj, and B. Yeganarayana, “Genetic algorithm based optimal power flow for security enhancement”, IEE proc-Generation.Transmission and. Distribution; 152, 6 November 2005. DOI: https://doi.org/10.1049/ip-gtd:20045234

C.A. Caviars, A.C.Z.de Souza and V.H. Quintana,“Comparison of performance indices for detection of proximity to voltage collapse,’’ vol. 11. no.3, pp.1441-1450, Aug 1996 .

C. Dai, Y. Zhu, W. Chen: Seeker Optimization Algorithm, Lecture Notes in Computer Science, 4456, 167-176 (2007)

C. Dai, W. Chen, Y. Song, Y. Zhu: Seeker optimization algorithm: a novel stochastic search algorithm for global numerical optimization, Journal of Systems Engineering and Electronics, 21(2), 300-311 (2010) DOI: https://doi.org/10.3969/j.issn.1004-4132.2010.02.021

C. Dai, W. Chen, Y. Zhu, Z. Jiang, Z. You: Seeker optimization algorithm for tuning the structure and parameters of neural networks, Neurocomputing, 74(6), 876-883 (2011) DOI: https://doi.org/10.1016/j.neucom.2010.08.025

C. Dai, W. Chen, Y. Zhu: Seeker optimization algorithm for digital IIR filter design, IEEE Transactions on Industrial Electronics, 57(5), 1710-1718 (2010) DOI: https://doi.org/10.1109/TIE.2009.2031194

C. Dai, Z. Cheng, Q. Li, Z. Jiang, J. Jia: Seeker optimization algorithm for global optimization: A case study on optimal modelling of proton exchange membrane fuel cell (PEMFC), International Journal of Electrical Power and Energy Systems, 33(3), 369-376 (2011) DOI: https://doi.org/10.1016/j.ijepes.2010.08.032

C. Dai, W. Chen, L. Ran, Y. Zhang, Y. Du: Human Group Optimizer with Local Search, Lecture Notes in Computer Science, 6728, 310-320 (2011)

D. Karaboga, B. Basturk: A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (abc) algorithm, Journal of Global Optimization, 39(3), 459- 471 (2007) DOI: https://doi.org/10.1007/s10898-007-9149-x

N. Bacanin, M. Tuba: Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators, Studies in Informatics and Control, 21(2), 137-146 (2012) DOI: https://doi.org/10.24846/v21i2y201203

I.Brajevic, M. Tuba: An upgraded artificial bee colony algorithm (ABC) for constrained optimization problems, Journal of Intelligent Manufacturing, published Online First, DOI: 10.1007/s10845-011-0621-6, (2012) DOI: https://doi.org/10.1007/s10845-011-0621-6

I. Brajevic, M. Tuba, M. Subotic: Performance of the improved artificial bee colony algorithm on standard engineering constrained problems, International Journal of Mathematics and Computers in Simulation, 5(2), 135-143 (2011)

N. Stanarevic, M. Tuba, N. Bacanin: Modified artificial bee colony algorithm for constrained problems optimization, International Journal of Mathematical Models and Methods in Applied Sciences, 5(3), 644-651 (2011)

N. Bacanin, M. Tuba, I. Brajevic: Performance of objectoriented software system for improved artificial bee colony optimization, International Journal of Mathematics and Computers in Simulation, 5(2), 154-162 (2011)

M. Subotic, M. Tuba, N. Stanarevic: Different approaches in parallelization of the artificial bee colony algorithm, International Journal of Mathematical Models and Methods in Applied Sciences, 5(4), 755-762 (2011).

Chaohua Dai, Weirong Chen, Yunfang Zhu, and Xuexia Zhang, “Seeker optimization algorithm for optimal reactive power dispatch,” IEEE Trans. Power Systems, Vol. 24, No. 3, August 2009, pp. 1218-1231. DOI: https://doi.org/10.1109/TPWRS.2009.2021226

J. R. Gomes and 0. R. Saavedra, “Optimal reactive power dispatch using evolutionary computation: Extended algorithms,” IEE Proc.-Gener. Transm. Distrib.. Vol. 146, No. 6. Nov. 1999. DOI: https://doi.org/10.1049/ip-gtd:19990683

IEEE, “The IEEE 30-bus test system and the IEEE 118-test system”, (1993),


Jiangtao Cao, Fuli Wang and Ping Li, “An Improved Biogeography-based Optimization Algorithm for Optimal Reactive Power Flow”, International Journal of Control and Automation Vol.7, No.3 (2014), pp.161-176.




How to Cite

Lenin, K. (2017). ENHANCED SEEKER OPTIMIZATION ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS. International Journal of Research -GRANTHAALAYAH, 5(10), 336–347. https://doi.org/10.29121/granthaalayah.v5.i10.2017.2310

Most read articles by the same author(s)

1 2 3 4 5 6 7 > >>