Abstract
This paper presents an effective method of transmission line management in power systems. Two conflicting objectives 1) generation cost and 2) transmission line overload are optimized to provide non-dominated Pareto-optimal solutions. A fuzzy ranking-based multi-objective differential evolution (MODE) is used to solve this complex nonlinear optimization problem. The generator real power and generator bus voltage magnitude is taken as control variables to minimize the conflicting objectives. The fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed method has been analyzed on standard IEEE 30 bus system with smooth cost functions and their results are compared with non-dominated sorting genetic algorithm-II (NSGA-II) and Differential evolution (DE). The results demonstrate the superiority of the MODE as a promising multi-objective evolutionary algorithm to solve the power system multi-objective optimization problem.
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References
Alsac, O., Scott, B.: Optimal load flow with steady state security. IEEE Transactions on Power Systems 93(3), 745–751 (1974)
Stott, B., Hobson, E.: Power system security control calculations using linear programming. IEEE Transactions on Power Systems 97, 1713–1931 (1978)
Todorovski, M., Rajicic, D.: An Initialization Procedure in Solving Optimal Power Flow by Genetic Algorithm. IEEE Transactions on Power Systems 21(2), 480–487 (2006)
Abido, M.A.: Optimal power flow using particle swarm optimization. International Journal of Electrical Power and Energy Systems 24(7), 563–571 (2002)
Varadarajan, M., Swarup, K.: Solving multi-objective optimal power flow using differential evolution. IET, Generation, Transmission and Distribution 2(5), 720–730 (2008)
Duman, S., Guvenc, U., Sonmez, Y., Yorukeren, N.: Optimal power flow using gravitational search algorithm. Energy Conversion and Management 39, 86–95 (2012)
Abido, M.A.: Optimal power flow using tabu search algorithm. Electric Power Components and Systems 30 (2002)
Sumpavakup, C., Chusanapiputt, S.: A Solution to the Optimal Power Flow Using Artificial Bee Colony Algorithm. In: International Conference on Power System Technology, pp. 1–5 (2010)
Udupa, A.N., Purushothama, G.K., Parthasarathy, K., Thukaram, D.: A fuzzy control for network overload alleviation. International Journal of Electrical Power and Energy Systems 23(2), 119–128 (2001)
Sreejith, S., Psimon, S., Selvan, M.P.: Optimal location of Interline Power Flow Controller in a power system network using ABC algorithm. Archives of Electrical Engineering 62(1), 91–110 (2013)
Lu, Y., Abur, A.: Static Security Enhancement via Optimal Utilization of Thyristor-Controlled Series Capacitors. IEEE Transactions on Power Systems 17(2), 324–329 (2002)
Abou, E.L., Ela, A.A., Spea, S.R.: Optimal corrective actions for power systems using multi-objective genetic algorithms. Electric Power System Research 79(5), 722–733 (2009)
Hazra, J., Sinha, A.K.: Congestion management using multi objective particle swarm optimization. IEEE Transactions on Power Systems 22(4), 1726–1734 (2007)
Ghahremani, E., Kamwa, I.: Optimal Placement of Multiple-Type FACTS Devices to Maximize Power System Loadability Using a Generic Graphical User Interface. IEEE Transactions on Power Systems 28(2), 764–778 (2013)
Abido, M.A.: Multi-objective Evolutionary Algorithms for Electric Power Dispatch Problem. IEEE Transactions on Evolutionary Computation 11(43) (2006)
Gnanambal, K., Babulal, C.K.: Maximum loadability limit of power system using hybrid differential evolution with particle swarm optimization. Electrical Power and Energy Systems 43(1), 150–155 (2012)
Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P.N., Zhangd, Q.: Multi-objective Evolutionary Algorithms: A Survey of the State-of-the-art. Swarm and Evolutionary Computation 1(1), 32–49 (2011)
Xue, F., Sanderson, A.C., Graves, R.J.: Multi-objective-based multi-objective differential evolution. In: Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003), Canberra, Australia, vol. 2, pp. 862–869 (2003)
Zimmerman, R.D., Murillo-Sanchez, C.E., Gan, D.(D.): MATPOWER: A MATLAB Power System Simulation Package, http://www.pserc.cornell.edu/matpower/
Narmatha Banu, R., Devaraj, D.: Multi-objective Evolutionary Algorithm for Security Enhancement. Journal of Electrical Systems 5(4) (2009)
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Pandiarajan, K., Babulal, C.K. (2013). Transmission Line Management Using Multi-objective Evolutionary Algorithm. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8297. Springer, Cham. https://doi.org/10.1007/978-3-319-03753-0_29
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DOI: https://doi.org/10.1007/978-3-319-03753-0_29
Publisher Name: Springer, Cham
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