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Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization

DOI: 10.1155/2014/148204

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Abstract:

A cost effective off-line method for equivalent circuit parameter estimation of an induction motor using hybrid of genetic algorithm and particle swarm optimization (HGAPSO) is proposed. The HGAPSO inherits the advantages of both genetic algorithm (GA) and particle swarm optimization (PSO). 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 nameplate data or experimental tests. In this paper, the problem formulation uses the starting torque, the full load torque, the maximum torque, and the full load power factor which are normally available from the manufacturer data. The proposed method is used to estimate the stator and rotor resistances, the stator and rotor leakage reactances, and the magnetizing reactance in the steady-state equivalent circuit. The optimization problem is formulated to minimize an objective function containing the error between the estimated and the manufacturer data. The validity of the proposed method is demonstrated for a preset model of induction motor in MATLAB/Simulink. Also, the performance evaluation of the proposed method is carried out by comparison between the results of the HGAPSO, GA, and PSO. 1. Introduction In most of the applications, induction motors are preferred to DC motors, because of their simple structure, easy operation, and also low cost maintenance and durability. Recently, advanced control techniques such as vector control and field-oriented control (FOC) make them compete with DC motors in many aspects [1]. The information regarding motor circuit parameters is very important for design, performance evaluation, and feasibility of these control techniques. The conventional techniques for estimating the induction motor parameters are based on the locked-rotor and the no-load tests. However, these techniques cannot be implemented easily. The main disadvantage of these techniques is that the motor has to be locked mechanically. In the locked-rotor condition, the frequency of the rotor is equal to the supply frequency, but under operating condition, the rotor frequency is about 1–3?Hz. This incorrect rotor frequency will cause bad results for the locked-rotor test. Besides, in the motors with high power, this test is impractical. These problems have encouraged the researchers to investigate alternative techniques for parameter estimation. The problem of induction motor parameter estimation has been addressed extensively by many researchers in the past. Deep bar

References

[1]  K. S. Huang, W. Kent, Q. H. Wu, and D. R. Turner, “Parameter identification for FOC induction motors using genetic algorithms with improved mathematical model,” Electric Power Components and Systems, vol. 29, no. 3, pp. 247–258, 2001.
[2]  P. Castaldi and A. Tilli, “Parameter estimation of induction motor at standstill with magnetic flux monitoring,” IEEE Transactions on Control Systems Technology, vol. 13, no. 3, pp. 386–400, 2005.
[3]  F. Therrien, L. Wang, J. Jatskevich, and O. Wasynczuk, “Efficient explicit representation of AC machines main flux saturation in state-variable-based transient simulation packages,” IEEE Transactions on Energy Conversion, vol. 28, no. 2, pp. 380–393, 2013.
[4]  B. Ouyang, D. Liu, X. Zhai, and W. Ma, “Analytical calculation of inductances of windings in electrical machines with slot skew,” in Proceedings of the 19th International Conference on Electrical Machines (ICEM '10), pp. 1–6, September 2010.
[5]  V. P. Sakthivel, R. Bhuvaneswari, and S. Subramanian, “Multi-objective parameter estimation of induction motor using particle swarm optimization,” Engineering Applications of Artificial Intelligence, vol. 23, no. 3, pp. 302–312, 2010.
[6]  A. I. Canakoglu, A. G. Yetgin, H. temurtas, and M. Turan, “Induction motor parameter estimation using metaheuristic methods,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 22, pp. 1177–1192, 2014.
[7]  M. A. Awadallah, “Parameter estimation of induction machines from nameplate data using particle swarm optimization and genetic algorithm techniques,” Electric Power Components and Systems, vol. 36, no. 8, pp. 801–814, 2008.
[8]  D. E. Goldberg, Genetic Algorithms in Search Optimization and Machine Learning, Addison-Wesley, 1989.
[9]  F. Filho, H. Z. Maia, T. H. A. Mateus, B. Ozpineci, L. M. Tolbert, and J. O. P. Pinto, “Adaptive selective harmonic minimization based on ANNs for cascade multilevel inverters with varying DC sources,” IEEE Transactions on Industrial Electronics, vol. 60, no. 5, pp. 1955–1962, 2013.
[10]  F. J. Lin and P. K. Huang, “Recurrent fuzzy neural network using genetic algorithm for linear induction motor servo drive,” in Proceedings of the 1st IEEE Conference on Industrial Electronics and Applications (ICIEA '06), pp. 1–6, Singapore, May 2006.
[11]  J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, December 1995.
[12]  H. Taghizadeh and M. T. Hagh, “Harmonic elimination of cascade multilevel inverters with nonequal dc sources using particle swarm optimization,” IEEE Transactions on Industrial Electronics, vol. 57, no. 11, pp. 3678–3684, 2010.
[13]  M. Clerc and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58–73, 2002.
[14]  T. Back and H. P. Schwefel, “An overview of evolutionary algorithms for parameter optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 1–23, 1993.
[15]  C.-F. Juang, “A hybrid of genetic algorithm and particle swarm optimization for recurrent network design,” IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, vol. 34, no. 2, pp. 997–1006, 2004.
[16]  K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, New York, NY, USA, 2001.

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