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On-site efficiency evaluation of three-phase induction motor based on particle swarm optimization

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  • Sakthivel, V.P.
  • Subramanian, S.

Abstract

On-site efficiency determination of induction motor is essential in industrial plants for saving the energy consumption. This paper presents a new application of particle swarm optimization (PSO) approach for field efficiency evaluation of induction motor based on a modified induction motor equivalent circuit. The stray-load loss is considered in the equivalent circuit by adding an equivalent resistor in series with the rotor circuit and its value is derived from the assumed stray-load loss recommended in IEEE Std. 112. The PSO approach uses the information about the stator current, stator voltage, input power, stator resistance and speed of the motor and determines the equivalent circuit parameters. Once these parameters are known, the efficiency of motor can be evaluated. The simulation results on a 3.75kW motor are presented and compared with the results of torque gauge method (TGM), equivalent circuit method (ECM), slip method (SM), current method (CM) and segregated loss method (SLM). The results reveal that the proposed method can evaluate the efficiencies of motor with less than 3% error under normal load conditions. Consequently, the method can be used in motor energy management system for improving the overall energy savings in industry.

Suggested Citation

  • Sakthivel, V.P. & Subramanian, S., 2011. "On-site efficiency evaluation of three-phase induction motor based on particle swarm optimization," Energy, Elsevier, vol. 36(3), pages 1713-1720.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:3:p:1713-1720
    DOI: 10.1016/j.energy.2010.12.057
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    Citations

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    Cited by:

    1. Nafar, M. & Gharehpetian, G.B. & Niknam, T., 2011. "Improvement of estimation of surge arrester parameters by using Modified Particle Swarm Optimization," Energy, Elsevier, vol. 36(8), pages 4848-4854.
    2. El-Kharashi, Eyhab, 2014. "Detailed comparative study regarding different formulae of predicting the iron losses in a machine excited by non-sinusoidal supply," Energy, Elsevier, vol. 73(C), pages 513-522.
    3. El-Kharashi, Eyhab & Farid, Azmy Wadie, 2015. "Accurate assessment of the output energy from the doubly fed induction generators," Energy, Elsevier, vol. 93(P1), pages 406-415.
    4. Guo, Jingquan & Ma, Xinqiang & Ahmadpour, Ali, 2021. "Electrical–mechanical evaluation of the multi–cascaded induction motors under different conditions," Energy, Elsevier, vol. 229(C).
    5. Myeong-Hwan Hwang & Hae-Sol Lee & Se-Hyeon Yang & Hyun-Rok Cha & Sung-Jun Park, 2019. "Electromagnetic Field Analysis and Design of an Efficient Outer Rotor Inductor in the Low-Speed Section for Driving Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-19, December.
    6. El-Kharashi, Eyhab & Massoud, Joseph Girgis & Al-Ahmar, M.A., 2019. "The impact of the unbalance in both the voltage and the frequency on the performance of single and cascaded induction motors," Energy, Elsevier, vol. 181(C), pages 561-575.
    7. Lei, Fei & Bai, Yingchun & Zhu, Wenhao & Liu, Jinhong, 2019. "A novel approach for electric powertrain optimization considering vehicle power performance, energy consumption and ride comfort," Energy, Elsevier, vol. 167(C), pages 1040-1050.
    8. Lei, Fei & Du, Bin & Liu, Xin & Xie, Xiaoping & Chai, Tian, 2016. "Optimization of an implicit constrained multi-physics system for motor wheels of electric vehicle," Energy, Elsevier, vol. 113(C), pages 980-990.
    9. Lei, Fei & Gu, Ke & Du, Bin & Xie, Xiaoping, 2017. "Comprehensive global optimization of an implicit constrained multi-physics system for electric vehicles with in-wheel motors," Energy, Elsevier, vol. 139(C), pages 523-534.
    10. El-Kharashi, Eyhab & El-Dessouki, Maher, 2014. "Coupling induction motors to improve the energy conversion process during balanced and unbalanced operation," Energy, Elsevier, vol. 65(C), pages 511-516.

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