Non-Invasive Equivalent Circuit Method for Three-Phase Induction Motor Efficiency Estimation using Particle Swarm Optimization

Keerati Chayakulkheeree, Vichakorn Hengsritawat, Petch Nantivatana, Preecha Kocharoen


In this paper, the on-service non-invasive efficiency estimation using equivalent circuit (EC) for three-phase induction motor replacement program is presented and investigated. The motor equivalent circuit parameters (ECPs) are estimated by particle swarm optimization (PSO) using measurement data during on-service condition. Then, the non-invasive equivalent circuit method (NIECM) for motor efficiency estimation can be performed using the PSO based motor ECPs estimation. In the proposed NIECM, the induction motor ECPs are estimated by using the measured motor voltage, current, real and reactive powers, power factor, and speed. Therefore, the motor efficiency can be non-invasively analyzed. The developed NIECM software has been tested with nine motors in the laboratory and investigated with five motor replacement programs. The experimentation results of the proposed NIECM, comparing to conventional slip method (SM) and current method (CM), are illustrated and discussed. Among NIECM, CM, and SM, the proposed NIECM provide the minimum error in efficiency estimation comparing to the shaft-torque method. Therefore, the proposed NIECM can, potentially and conveniently, be applied for the on-service non-invasive three-phase induction motor efficiency estimations, with the reasonable mismatch to the laboratory shaft-torque method.


equivalent circuit method; current method; non-invasive induction motor efficiency estimation; particle swarm optimization; slip method

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IEEE Standard Test Procedure for Polyphase Induction Motors and Generators, IEEE Standard 112-2004.

International Standard, Rotating Electrical Machines, IEC60034-2-1, 2014.

U.S. Department of Energy. Determining Electric Motor Load and Efficiency. Motor Challenge, DOE/GO-10097-517.

Wallace A., Jouanne A., von Wiedenbrug E., Douglass J., Wohlgemuth C., and Wainwright G.A, 1997. Laboratory assessment of in-service motor efficiency testing methods. IEEE International on Electric Machines and Drives Conference Record. Milwaukee, WI, USA, pp. 18-21.

Dlamini V., Naidoo R., and Manyage M., 2010. Practical evaluation of motors efficiency estimation techniques. Proceedings of the International Conference on the Industrial and Commercial Use of Energy (ICUE), August, pp. 3-9.

Hsu J.S., Kueck J.D., Olszewski M., Casada D.A., Otaduy P.J., and Tolbert L.M., 1998. Comparison of induction motor field efficiency evaluation methods. IEEE Transaction on Industry Application 34(1): 117-125.

Lu B., Habetler T.G., and Harley R.G., 2006. A survey of efficiency-estimation methods for in-service induction motors. IEEE Transaction on Industry Application 42(4): 924-932.

Holmquist J.R., Rooks J.A., and Richter M.E., 2004. Practical approach for determining motor efficiency in the field using calculated and measured values. IEEE Transaction on Industry Application 40(1): 242-248.

Salomon C.P., Sant’Ana W.C., Torres G.L., da Silva L.E.B., Bonaldi E.L., and de Oliveira L.E.L., 2018. Comparison among methods for induction motor low-intrusive efficiency evaluation including a new agt approach with a modified stator resistance. Energies 11(691): 1-21. doi:10.3390/en11040691.

Hsu J.S. and B.P. Scoggins. 1995. field test of motor efficiency and load changes through air-gap torque. IEEE Transaction on Energy Conversion, 10(3): 471-477.

Chayakulkheereea K., Hengsritawat V., and Nantivatana P., 2017. Particle swarm optimization based equivalent circuit estimation for on-service three-phase induction motor efficiency assessment. Engineering Journal 21(6): 101-110.

Aspalli M.S., Shetagar S.B., and Kodad S.F., 2008. Estimation of induction motor field efficiency for energy audit and management using genetic algorithm. In 3rd International Conference on Sensing Technology, Nov. 30 – Dec. 3, Tainan, Taiwan. pp. 440-445.

Wengerkievicz C.A.C., de Elias A.R., Batistela N.J., Sadowski N., Kuo-Peng P., Lima S.C., da Silva Jr. P.A., and Beltrame A.Y., 2017. Estimation of three-phase induction motor equivalent circuit parameters from manufacturer catalog data. Journal of Microwaves, Optoelectronics and Electromagnetic Applications, 16(1): 90-107.

Alonge F., D’Ippolito F., Ferrante G., Raimondi F. M., 1998. Parameter identification of induction motor model using genetic algorithm. IEE Proc-Control Theory Applications 145(6): 587-593.

Jangjit S. and P. Laohachai. 2009. Parameter estimation of three-phase induction motor by using genetic algorithm. Journal of Electrical Engineering and Technology 4(3): 360-364.

Kostov I., Spasov V., and Rangelova V., 2009. Application of genetic algorithms for determining the parameters of induction motors. Technical Gazette 16(2): 49-53.

Emara H.M., Elshamy W., and Bahgat A., 2008. Parameter identification of induction motor using modified particle swarm optimization algorithm. Proceeding of IEEE Industrial Electronics International Symposium (ISIE), June, pp. 841–847.

Rashag H.F., Koh S.P., Tiong S.K., Chong K.H., and Abdalla A.N., 2011. Investigation of induction motor parameter identification using particle swarm optimization-based RBF neural network (PSORBFNN). International Journal of the Physical Sciences 6(19): 4564-4570.

Sakthivel V.P., Bhuvanneswari R., and Subramanian S., 2010. An improved particle swarm optimization for induction motor parameter determination. International Journal of Computer Application 1(2): 62-67.

Picardi C. and N. Rogano. 2006. Parameter identification of induction motor based on particle swarm optimization. Proceeding of IEEE Power Electronic, Electrical Drives, Automation and Motion International Symposium (SPEEDAM), May, pp. 968–973.

RezaMohammadi H. and A. Akhavan. 2014. Parameter estimation of three-phase induction motor using hybrid of genetic algorithm and particle swarm optimization. Journal of Engineering, pp.1-6.

Canakoglu A.I., Yetgin A.G., Temurtas H., and Turan, 2014. M. Induction motor parameter estimation using metaheuristic methods. Turkish Journal of Electrical Engineering & Computer Sciences 22: 1177-1192.

Avalos O., Cuevas E., Gálvez J., 2016. Induction motor parameter identification using a gravitational search algorithm. Computer 5(6): 1-12.

Grewal G.S. and B. Singh. 2018. Efficiency determination of in-service induction machines using gravitational search optimization. Measurement 118: 156-163.

Kennedy J. and R. Eberthart. 1995. Particle swarm optimization. Proceeding IEEE International Conference on Neural Networks, Vol. IV, pp. 1942-1948.