Abstract
This paper projects enhanced red wolf optimization (ERWO) algorithm for solving optimal reactive power problem. Projected ERWO algorithm hybridizes the wolf optimization (WO) algorithm with particle swarm optimization (PSO) algorithm. Each red wolf has a flag vector, in the algorithm, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). Due to the hybridization of both WO with PSO exploration, the ability of the proposed red wolf optimization algorithm has been enhanced. Efficiency of the projected enhanced red wolf optimization (ERWO) algorithm has been evaluated in standard IEEE 118 bus test system. Results indicate that enhanced red wolf optimization (ERWO) algorithm performs well in solving the problem. Actual power losses are reduced, and control variables are well within the limits.
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References
Alsac, O., Scott, B.: Optimal load flow with steady state security. IEEE Trans. PAS, 745–751 (1973)
Lee, K.Y., Paru, Y.M., Oritz, J.L.: A united approach to optimal real and reactive power dispatch. IEEE Trans. Power Apparatus Syst.: PAS-104, 1147–1153 (1985)
Monticelli, A., Pereira, M.V.F., Granville, S.: Security constrained optimal power flow with post contingency corrective rescheduling. IEEE Trans. Power Syst:PWRS-2(1), 175–182 (1987)
Deeb, N., Shahidehpur, S.M.: Linear reactive power optimization in a large power network using the decomposition approach. IEEE Trans Power Syst. 5(2), 428–435 (1990)
Hobson, E.: Network constrained reactive power control using linear programming. IEEE Trans. Power Syst. PAS-99(4), 868–877 (1980)
Lee, K.Y., Park, Y.M., Oritz, J.L.: Fuel-cost optimization for both real and reactive power dispatches. IEE Proc 131C(3), 85–93 (1984)
Mangoli, M.K., Lee, K.Y.: Optimal real and reactive power control using linear programming. Electr. Power Syst. Res. 26, 1–10 (1993)
Canizares, C.A., de Souza, A.C.Z., Quintana, V.H.: Comparison of performance indices for detection of proximity to voltage collapse. 11(3), 1441–1450 (1996)
Anburaja, K.: Optimal power flow using refined genetic algorithm. Electr. Power Compon. Syst 30, 1055–1063 (2002)
Devaraj, D., Yeganarayana, B.: Genetic algorithm based optimal power flow for security enhancement. IEE Proc.-Gen. Transm. Distrib. 152 (2005)
Berizzi, A., Bovo, C., Merlo, M., Delfanti, M.: A ga approach to compare orpf objective functions including secondary voltage regulation. Electr. Power Syst. Res. 84(1), 187–194 (2012)
Yang, C.-F., Lai, G.G., Lee, C.-H., Su, C.-T., Chang, G.W.: Optimal setting of reactive compensation devices with an improved voltage stability index for voltage stability enhancement. Int. J. Electr. Power Energy Syst. 37(1), 50–57 (2012)
Roy, P., Ghoshal, S., Thakur, S.: Optimal var control for improvements in voltage profiles and for real power loss minimization using biogeography based optimization. Int. J. Electr. Power Energy Syst. 43(1), 830–838 (2012)
Venkatesh, B., Sadasivam, G., Khan, M.: A new optimal reactive power scheduling method for loss minimization and voltage stability margin maximization using successive multi-objective fuzzy lp technique. IEEE Trans. Power Syst. 15(2), 844–851 (2000)
Yan, W., Lu, S., Yu, D.: A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique. IEEE Trans. Power Syst. 19(2), 913–918 (2004)
Yan, W., Liu, F., Chung, C., Wong, K.: A hybrid genetic algorithm interior point method for optimal reactive power flow. IEEE Trans. Power Syst. 21(3), 1163–1169 (2006)
Yu, J., Yan, W., Li, W., Chung, C., Wong, K.: An unfixed piecewise optimal reactive power-flow model and its algorithm for ac-dc systems. IEEE Trans. Power Syst. 23(1), 170–176 (2008)
Capitanescu, F.: Assessing reactive power reserves with respect to operating constraints and voltage stability. IEEE Trans. Power Syst. 26(4), 2224–2234 (2011)
Hu, Z., Wang, X., Taylor, G.: Stochastic optimal reactive power dispatch: Formulation and solution method. Int. J. Electr. Power Energy Syst. 32(6), 615–621 (2010)
Kargarian, A., Raoofat, M., Mohammadi, M.: Probabilistic reactive power procurement in hybrid electricity markets with uncertain loads. Electr. Power Syst. Res. 82(1), 68–80 (2012)
Kaveh, A., Shokohi, F.: Application of Grey Wolf Optimizer in design of castellated beams. Asian J. Civil Eng. 17(5), 683–700 (2016)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceeding of IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948 (1995)
IEEE: The IEEE 30-bus test system and the IEEE 118-test system (1993). http://www.ee.washington.edu/trsearch/pstca/
Cao, Jiangtao, Wang, Fuli, Li, Ping: An improved biogeography-based optimization algorithm for optimal reactive power flow. Int. J. Control Autom. 7(3), 161–176 (2014)
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Lenin, K. (2019). Enhanced Red Wolf Optimization Algorithm for Reduction of Real Power Loss. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 104. Springer, Singapore. https://doi.org/10.1007/978-981-13-1921-1_5
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DOI: https://doi.org/10.1007/978-981-13-1921-1_5
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