Abstract
Genetic algorithms are widely recognized as efficient tool for solution of complex and non-linear optimization problem of optimal reactive power dispatch and voltage control in power systems. The paper is addressed to the consideration of influence of different methods for generating initial population for genetic algorithm performance. Genetic algorithm operates on individuals representing some solutions. Randomly generated initial population evolve during the evolutionary process with use of some operations into the final population including probably the best task solution. The way of creating initial population decides on covering initial solution space. Hence, it may affect genetic algorithm performance. There exist variety of methods to generate members of initial population covering solution space. The paper presents an evaluation of pseudo-random numbers, gaussian and some space point process based algorithms to produce initial population in terms of the convergence speed and quality of the obtained optimization results.
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References
Iba, K.: Reactive power optimization by genetic algorithm. IEEE Trans. Power Syst. 9(2), 685–692 (1994)
Wu, Q.H., Ma, J.T.: Power system optimal reactive power dispatch using evolutionary programming. IEEE Trans. Power Syst. 10(3), 1243–1249 (1995)
Yan, W., Liu, F., Chung, C.Y., Wong, K.P.: A hybrid genetic algorithm-interior point method for optimal reactive power flow. IEEE Trans. Power Syst. 21(3), 1163–1169 (2006)
Yan, W., Lu, S., Yu, D.C.: A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique. IEEE Trans. Power Syst. 19(2), 913–918 (2004)
Das, D.B., Patvardhan, C.: A new hybrid evolutionary strategy for reactive power dispatch. Electr. Power Syst. Res. 65(2), 83–90 (2003)
Liang, C.H., Chung, C.Y., Wong, K.P., Duan, X.Z., Tse, C.T.: Study of differential evolution for optimal reactive power flow. IET Gen. Transm. Disrib. 1(2), 253–260 (2007)
Subbaraj, P., Rajnarayanan, P.N.: Optimal reactive power dispatch using self-adaptive real coded genetic algorithm. Electr. Power Syst. Res. 79(2), 374–381 (2009)
Zhihuan, L., Yinhong, L., Xianzhong, D.: Improved strength Pareto evolutionary algorithm with local search strategies for optimal reactive power flow. Inf. Technol. J. 9(4), 749–757 (2010)
Maaranen, H., Miettinen, K., Penttinen, A.: On initial populations of a genetic algorithm for continuous optimization process. J. Global Optim. 37, 405–436 (2007)
Kimura, S., Matsumura, K.: Genetic algorithms using low-discrepancy sequences. Proceedings of the Genetic and Evolutionary Computation Conference, New York, pp. 1341–1346 (2005)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolutionary Programs, Springer, Berlin (1996)
Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley Press, Hoboken, pp. 113–125 (2002)
Marsaglia, G., Zaman, A.: A new class of random number generators. Ann. Appl. Prob. 1(3), 462–480 (1994)
Niederreiter, H.: Low-discrepancy and low-dispersion sequences. J. Number Theory 30, 51–70 (1988)
Lee, K., Park, Y., Ortiz, J.: A united approach to optimal real and reactive power dispatch. IEEE Trans. PAS-104, pp. 1147–1153 (1985)
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Łukomski, R. (2016). Using Genetic Algorithm for Optimal Dispatching of Reactive Power in Power Systems. In: Wilimowska, Z., Borzemski, L., Grzech, A., Świątek, J. (eds) Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology – ISAT 2015 – Part IV. Advances in Intelligent Systems and Computing, vol 432. Springer, Cham. https://doi.org/10.1007/978-3-319-28567-2_16
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DOI: https://doi.org/10.1007/978-3-319-28567-2_16
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