Abstract
An offline parameter estimation problem of an induction motor using a well known, efficient yet simple meta heuristic algorithm DEGL (Differential Evolution with a neighborhood based mutation scheme) has been presented in this article. Two different induction motor models such as approximate and exact models are considered. The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the manufacturer data or from tests. Differential Evolution is not completely free from the problems of slow or premature convergence, that’s why the idea of a much more efficient variant of DE comes. The variant of DE used for solving this problem utilize the concept of the neighborhood of each population member. The feasibility of the proposed method is demonstrated for two different motors and it is compared with the genetic algorithm and the Particle Swarm Optimization algorithm. From the simulation results it is evident that DEGL outperforms both the algorithms (GA and PSO) in the estimation of the parameters of the induction motor.
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References
Say, M.G.: Alternating Current Machines. Pitman (1983)
Koubaa, Y.: Recursive identification of induction motor parameters. Journal of Simulation Modeling Practice and Theory 12(5), 363–381 (2004)
Stephan, J., Bodson, M., Chiasson, J.: Real time estimation of induction motor parameters. IEEE Transactions on Industry Appications 30(3), 746–759 (1994)
Pedra, J., Sainz, L.: Parameter estimation of squirrel-cage induction motors without torque measurements. IEE Proceedings on Electric Power Application 153(2), 263–269 (2006)
Johnson, B.K., Willis, J.R.: Tailoring induction motor analytical models to fit known motor performance characteristics and satisfy particular study needs. IEEE Transactions on Power Systems 6(3), 959–965 (1991)
Cirrincione, M., Pucci, M., Cirrincione, G., Capolino, G.A.: A new experimental application of laest aquares techniques for the estimation of the induc- tion motor parameters. IEEE Transactions on Industry Applications 39(5), 1247–1256 (2003)
Pillay, P., Nollan, R., Haque, T.: Application of genetic algorithms to motor parameter determination for transient torque calculations. IEEE Transactions on Industry Applications 33(5) (1997)
Nangsue, P., Pillay, P., Conry, S.: Evolutionary algorithms for induction motor parameter determination. IEEE Transactionson Energy Conversion 14(3), 447–453 (1999)
Bishop, R.R., Richards, G.G.: Identifying induction machine parameters using a genetic optimization algorithm. In: Proceedings on IEEE South east conference, vol. 2, pp. 476–479 (1990)
Alonge, F., Dippolito, F., Ferrante, G., Raimondi, F.M.: Parameter identification of induction motor model using genetic algorithms. In: IEE Proceedings on Control Theory Applications, vol. 145(6) (1998)
Rahimpour, E., Rashtchi, V., Pesaran, M.: Parameter identification of deep-bar induction motors using genetic algorithm. Electrical Engineering 89, 547–552 (2007)
Orlowska Kowalska, T., Lis, J., Szabat, K.: Identification of the induction motor parameters using soft computing methods. Computation and Mathematics in Electrical and Electronics Engineering 25(1), 181–192 (2006)
Abdelhadi, B., Benoudjit, A., Nait Said, N.: Identification of induction machine parameters using a adaptive genetic algorithm. Electric Power Components and Systems 32, 767–784 (2004)
Eberhart, R.C., Kennedy, J.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1947 (1995)
Storn, R., Price, K.V.: Differential Evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report TR-95-012,ICSI (1995), http://http.icsi.berkeley.edu/~storn/litera.html
Storn, R., Price, K.V.: Minimizing the real functions of the ICEC 1996 contest by differential evolution. In: Proceedings of the 1996 IEEE international conference on evolutionary computation, Nagoya, Japan, pp. 842–844. IEEE Press, New York (1996)
Das, S., Konar, A., Chakraborty, U.K., Abraham, A.: Differential evolution with a neighborhood based mutation operator: a comparative study. IEEE Transactions on Evolutionary Computation 13(3), 526–553 (2009)
Nollan, R., Pillay, P., Haque, T.: Application of genetic algorithms to motor parameter determination. In: Proceedings of 1994 IEEE-IAS conference Denvar, pp. 47–54 (1994)
Sakthivel, V.P., et al.: Multi-objective parameter estimation of induction motor using particle swarm optimization. Engineering Applications of Artificial Intelligence (2009)
Mendes, R., Kennedy, J.: The fully informed Particle Swarm: simpler, maybe better. IEEE Transaction on evolutionary Computation 8(3) (2004)
Eiben, A.E., et al.: Genetic algorithms with multi-parent recombination. In: PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature, pp. 78–87 (1994); ISBN 3-540-58484-6
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Giri, R., Chowdhury, A., Ghosh, A., Panigrahi, B.K., Das, S. (2010). Offline Parameter Estimation of Induction Motor Using a Meta Heuristic Algorithm. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_61
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DOI: https://doi.org/10.1007/978-3-642-17563-3_61
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