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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 130))

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Abstract

Due to robustness, reliability, low price and maintenance free, induction motors (IMs) are used in most of the industrial applications. The influence of these motors (in terms of energy consumption) in energy intensive industries is significant in total input cost. The exact knowledge of some of the induction motor parameters is very important to implement efficient control schemes and its in-situ efficiency determination. These parameters can be obtained by no-load test that is not easily possible for the motors working in process industries where continuous operation is required. Here, Particle Swarm Optimization is used for in-situ efficiency determination of induction motor (5 hp) without performing no-load test. Results are compared with physical efficiency measurement method. The error between estimated and actual efficiencies is found for different objective functions and for different standards.

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References

  1. Nasir Uddin, M., Nam, S.W.: Adaptive back stepping based online loss minimisation control of an IM drive. IEEE Trans. Power Electronics 23(2), 926 (2008)

    Article  Google Scholar 

  2. Liu, J., Sun, J., Xu, W.: Quantum-Behaved Particle Swarm Optimization with Adaptive Mutation Operator. In: Jiao, L., Wang, L., Gao, X.-b., Liu, J., Wu, F. (eds.) ICNC 2006, Part I. LNCS, vol. 4221, pp. 959–967. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  3. Bounekhla, M., Zaim, M.E., Rezzoug, A.: Comparative study of three minimization methods applied to the induction machine parameters identification using transient stator current. Electric Power Components and Systems 33, 913–930 (2005)

    Article  Google Scholar 

  4. Renier, B., Hameyer, K., Belmans, R.: Comparison of standards for determining efficiency of three phase induction motors. IEEE Journal (1999)

    Google Scholar 

  5. Nangsue, P., Pillay, P., Conry, S.E.: Evolutionary algorithms for induction motor parameter determination. IEEE Trans. Energy Conversion 14(3), 447–453 (1999)

    Article  Google Scholar 

  6. Benaidja, N., Khenfer, N.: Identification of asynchronous machine parameters by evolutionary techniques. Electric Power Components and Systems 34, 1359–1376 (2006)

    Article  Google Scholar 

  7. Ursem, R.K., Vadstrup, P.: Parameter identification of induction motors using differential evolution. In: Proc. IEEE Congress on Evolutionary Computation, pp. 790–796. IEEE Press, New Jersey (2003)

    Google Scholar 

  8. Ursem, R.K., Vadstrup, P.: Parameter identification of induction motors using stochastic optimization algorithms. Applied Soft Computing 4(1), 49–64 (2004)

    Article  Google Scholar 

  9. IEEE Standard. IEEE Power Engineering Society, IEEE standard test procedure for poly phase induction motors and generators, IEEE Std: 112-2004 (November 4, 2004)

    Google Scholar 

  10. Auinger, H.: Determination and designation of the efficiency of electrical machines. Power Engineering Journal (1999)

    Google Scholar 

  11. Boglietti, A., Cavagnino, A., Lazzari, M., Pastorelli, M.: Induction Motor Efficiency Measurements in accordance to IEEE 112-B, IEC 34-2 and JEC-37 International Standards. IEEE Journal (2003)

    Google Scholar 

  12. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948. IEEE Service Center, Piscataway (1995)

    Google Scholar 

  13. Thangaraj, R., Thanga Raj, C., Pant, M., Nagar, A.K.: In-Situ Efficiency Determination of Induction Motor: A Comparative Study of Evolutionary Techniques. Application of Artificial Intelligence 25(2), 116–140 (2011)

    Article  Google Scholar 

  14. Pillay, P., Levin, V., Otaduy, P., Kueck, J.: In situ induction motor efficiency determination using genetic algorithm. IEEE Trans. Energy Conversion 13(4), 326–333 (1998)

    Article  Google Scholar 

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Correspondence to S. Anil Chandrakanth .

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© 2012 Springer India Pvt. Ltd.

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Chandrakanth, S.A., Chelliah, T.R., Srivastava, S.P., Thangaraj, R. (2012). In-situ Efficiency Determination of Induction Motor through Parameter Estimation. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 130. Springer, India. https://doi.org/10.1007/978-81-322-0487-9_66

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  • DOI: https://doi.org/10.1007/978-81-322-0487-9_66

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  • Print ISBN: 978-81-322-0486-2

  • Online ISBN: 978-81-322-0487-9

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