In this paper, an upgraded Red Shaver swarm Optimization (RS) algorithm is proposed for solving reactive power problem. Under cockerel as group-mate Red Shaver explores food; also it prevents the same ones to eat their own food. Red Shaver would arbitrarily pinch the high-quality food which has been already found by other Red Shaver & always overriding other individuals to grab more food. In the Projected upgraded Red Shaver swarm Optimization (RS) algorithm additional parameters of cockerel, hens and chicks are eliminated, in order to upsurge the search towards global optimization solution. Proposed Upgraded Red Shaver swarm Optimization (RS) algorithm has been tested in standard IEEE 30 bus system. Simulation results show clearly the better performance of the proposed RS algorithm in reduction of real power loss.
optimal reactive power, transmission loss, cockerel, upgraded red shaver swarm optimization
 Alsac O, Scott B. (1973). Optimal load flow with steady state security. IEEE Transaction. PAS -1973, 745-751.
 Lee KY, Paru YM, Oritz JL. (1985). A united approach to optimal real and reactive power dispatch. IEEE Transactions on Power Apparatus and Systems PAS-104, 1147-1153.
 Monticelli A, Pereira MVF, Granville S. (1987). Security constrained optimal power flow with post contingency corrective rescheduling. IEEE Transactions on Power Systems: PWRS-2 (1): 175-182.
 Deeb N, Shahidehpur SM. (1990). Linear reactive power optimization in a large power network using the decomposition approach. IEEE Transactions on Power System 5(2): 428-435.
 Hobson E. (1980). Network consrained reactive power control using linear programming. IEEE Transactions on Power Systems PAS 99(4): 868-877.
 Lee KY, Park YM, Oritz JL. (1984). Fuel –cost optimization for both real and reactive power dispatches. IEE Proc. 131C(3): 85-93.
 Mangoli MK, Lee KY. (1993). Optimal real and reactive power control using linear programming. Electr. Power Syst. Re. 26: 1-10.
 Anburaja K. (2002). Optimal power flow using refined genetic algorithm. Electr.Power Compon.Syst. 30: 1055- 1063.
 Berizzi A, Bovo C, Merlo M, Delfanti M. (2012). A GA approach to compare orpf objective functions including secondary voltage regulation. Electric Power Systems Research 84(1): 187–194.
 Yang CF, Lai GG, Lee CH, Su CT, Chang GW. (2012). Optimal setting of reactive compensation devices with an improved voltage stability index for voltage stability enhancement. International Journal of Electrical Power and Energy Systems 37(1): 50–57.
 Roy P, Ghoshal S, Thakur S. (2012). Optimal var control for improvements in voltage profiles and for real power loss minimization using biogeography based optimization. International Journal of Electrical Power and Energy Systems 43(1): 830–838.
 Venkatesh B, Sadasivam G, Khan M. (2000). A new optimal reactive power scheduling method for loss minimization and voltage stability margin maximization using successive multi-objective fuzzy lp technique. IEEE Transactions on Power Systems 15(2): 844–851.
 Yan W, Lu S, Yu D. (2004). A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique. IEEE Transactions on Power Systems 19(2): 913–918.
 Yan W, Liu F, Chung C, Wong K. (2006). A hybrid genetic algorithminterior point method for optimal reactive power flow. IEEE Transactions on Power Systems 21(3): 1163 –1169.
 Yu J, Yan W, Li W, Chung C, Wong K. (2008). An unfixed piecewiseoptimal reactive power-flow model and its algorithm for ac-dc systems. IEEE Transactions on Power Systems 23(1): 170 –176.
 Capitanescu F. (2011). Assessing reactive power reserves with respect to operating constraints and voltage stability. IEEE Transactions on Power Systems 26(4): 2224–2234. http://dx.doi.org/10.1109/TPWRS.2011.2109741
 Hu Z, Wang X, Taylor G. (2010). Stochastic optimal reactive power dispatch: Formulation and solution method. International Journal of Electrical Power and Energy Systems 32(6): 615–621. http://dx.doi.org/10.1016/j.ijepes.2009.11.018
 Kargarian A, Raoofat M, Mohammadi M. (2012). Probabilistic reactive power procurement in hybrid electricity markets with uncertain loads. Electric Power Systems Research 82(1): 68–80. http://dx.doi.org/10.1016/j.epsr.2011.08.019
 Zhou J, Tang BG, Ren XW. (2017). Research on prediction model for icing thickness of transmission lines based on bp neural network optimized with improved fruit fly algorithm. AMSE Journals-AMSE IIETA Publication Series: Advances 60(1): 255-269.
 Meng X, Liu Y, Gao X, Zhang H. (2014). A new bio-inspired algorithm: chicken swarm optimization. Advances in Swarm Intelligence SE 8794; 86–94.
 Wu QH, Cao YJ, Wen JY. (1998). Optimal reactive power dispatch using an adaptive genetic algorithm. Int. J. Elect. Power Energy Syst. 20: 563-569. http://dx.doi.org/10.1016/S0142-0615(98)00016-7
 Zhao B, Guo CX, Cao YJ. (2005). Multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Trans. Power Syst. 20(2): 1070-1078. http://dx.doi.org/10.1109/TPWRS.2005.846064
 Mahadevan K, Kannan PS. (2010). Comprehensive learning particle swarm optimization for reactive power dispatch. Applied Soft Computing 10(2): 641–52. http://dx.doi.org/10.1016/j.asoc.2009.08.038
 Khazali AH, Kalantar M. (2011). Optimal reactive power dispatch based on harmony search algorithm. Electrical Power and Energy Systems 33(3): 684–692. http://dx.doi.org/10.1016/j.ijepes.2010.11.018
 Sakthivel S, Gayathri M, Manimozhi V. (2013). A Nature inspired optimization algorithm for reactive power control in a power system. International Journal of Recent Technology and Engineering 2(1): 29-33.
 Tejaswini Sharma, Laxmi Srivastava,Shishir Dixit (2016). Modified Cuckoo Search Algorithm For Optimal Reactive Power Dispatch, Proceedings of 38 th IRF International Conference, pp. 4-8. Chennai, India, ISBN: 978-93-85973-76.