The Application of Memetic Algorithm for Induction Motor Parameter Estimation
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Published:
Jul 4, 2018
Keywords:
Parameter estimation Induction motor Memetic algorithm Genetic algorithm
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Abstract
This paper presents the estimation of the steady state equivalent circuit parameters of induction motor by memetic algorithm with shuffle frog-leaping technic for local search. Parameter estimation of the motors is calculated by the optimization technique using maximum torque, full load torque and starting torque data. The results of the proposed method are compared with genetic algorithm and shuffle frog-leaping algorithm. The results reveal that the parameter estimation of the motor using memetic algorithm has the mean percentage error 6.7 % compared with the parameters from the test in laboratory.
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How to Cite
ทิพจร ว., ไกลถิ่น ช., & เฉลิมกิจ จ. (2018). The Application of Memetic Algorithm for Induction Motor Parameter Estimation. Naresuan University Engineering Journal, 13(1), 43–52. Retrieved from https://ph01.tci-thaijo.org/index.php/nuej/article/view/73028
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Research Paper
References
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[2] Sakthivel, V. P., Bhuvaneswari, R., & Subramanian, S. (2010). Multi-objective parameter estimation of induction motor using particle swarm optimization. Engineering Applications of Artificial Intelligence, 23, 302-312.
[3] RezaMohammadi, H., & Akhavan, A. (2014). Parameter estimation of three-phase induction motor using hybrid of genetic algorithm and particle swarm optimization. Journal of Engineering, 2014.
[4] Duan, F. (2014). Induction motor parameters estimation and faults diagnosis using optimisation algorithms (Doctoral dissertation, The University of Adelaide, Australia). Retrieved from https://digital.library.adelaide.edu.au/dspace/bitstream/ 2440/92052/3/02whole.pdf
[5] Jangjit, S., & Laohachai, P. (2009). Parameter estimation of three-phase induction motor by using genetic algorithm. Journal of Electrical Engineering & Technology, 4(3), 360-364.
[6] Sakthivel, V. P., Bhuvaneswari, R., & Subramanian S. (2010). Artificial immune system for parameter estimation of induction motor. Expert Systems with Applications, 37, 6109–6115.
[7] Gomez-Gonzalez, M., Jurado, F., & Perez I. (2012). Shuffled frog-leaping algorithm for parameter estimation of a double-cage asynchronous machine. IET Electric Power Applications, 6, 484–490.
[8] Jamadi, M., & Merrikh-Bayat, F. (2014). New method for accurate parameter estimation of induction motors based on artificial bee colony algorithm. Retrieved from https://arxiv.org/ftp/arxiv/papers/1402/1402.4423.pdf
[9] Chompuming, S., Itthipong, P., & Pongcharoen, P. (2007). The application of shuffled frog-leaping technic to improve the performance of a memetic algorithm, Proceedings of National Operations Research Conference 2007, 162-174.
[10] Fitzgerald, A. E. (2005). Electric machinery (6th ed.). New York: McGraw-Hill.
[2] Sakthivel, V. P., Bhuvaneswari, R., & Subramanian, S. (2010). Multi-objective parameter estimation of induction motor using particle swarm optimization. Engineering Applications of Artificial Intelligence, 23, 302-312.
[3] RezaMohammadi, H., & Akhavan, A. (2014). Parameter estimation of three-phase induction motor using hybrid of genetic algorithm and particle swarm optimization. Journal of Engineering, 2014.
[4] Duan, F. (2014). Induction motor parameters estimation and faults diagnosis using optimisation algorithms (Doctoral dissertation, The University of Adelaide, Australia). Retrieved from https://digital.library.adelaide.edu.au/dspace/bitstream/ 2440/92052/3/02whole.pdf
[5] Jangjit, S., & Laohachai, P. (2009). Parameter estimation of three-phase induction motor by using genetic algorithm. Journal of Electrical Engineering & Technology, 4(3), 360-364.
[6] Sakthivel, V. P., Bhuvaneswari, R., & Subramanian S. (2010). Artificial immune system for parameter estimation of induction motor. Expert Systems with Applications, 37, 6109–6115.
[7] Gomez-Gonzalez, M., Jurado, F., & Perez I. (2012). Shuffled frog-leaping algorithm for parameter estimation of a double-cage asynchronous machine. IET Electric Power Applications, 6, 484–490.
[8] Jamadi, M., & Merrikh-Bayat, F. (2014). New method for accurate parameter estimation of induction motors based on artificial bee colony algorithm. Retrieved from https://arxiv.org/ftp/arxiv/papers/1402/1402.4423.pdf
[9] Chompuming, S., Itthipong, P., & Pongcharoen, P. (2007). The application of shuffled frog-leaping technic to improve the performance of a memetic algorithm, Proceedings of National Operations Research Conference 2007, 162-174.
[10] Fitzgerald, A. E. (2005). Electric machinery (6th ed.). New York: McGraw-Hill.