In-Service Induction Motor Effi ciency Estimation using Adaptive Bacteria Foraging Optimization
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Abstract
This paper proposes effi ciency estimation of in-service induction motor to replace the
effi ciency estimation that required to stop or remove them from service. Adaptive bacteria
foraging optimization technique is applied to estimate the effi ciency using data from
measuring electrical voltage, current, and power that supply to the induction motor
during operation. The selected motors for evaluation are the motor model in computer
and the actual motor. The test results showed that the effi ciency estimation of the adaptive
bacteria foraging optimization technique convergence is better result than original bacteria
foraging optimization and this result is close to the shaft torque method than the equivalent
circuit method and the slip method. The average error from the test of the motor model is
1.78 % and the actual motor is 3.4 %.
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References
Estimation Method for Energy Auditing and Management of In-Service Induction Motor using
Bacterial Foraging Algorithm. IET Electric Power Applications. Vol. 4, No. 8, pp. 579-590
[2] Wangsupapon, A., Phumiphak, P., and Chat-Uthai, C. (2007). Economical On-Site Effi ciency
Estimation Technique of Subway Tunnel Ventilation Fan Motor. Ladkrabang Engineering
Journal. Vol. 24, No. 1, pp. 7-12
[3] Lu, B., Habetler, T. G., and Harley, R. G. (2006). A Survey of Effi ciency-Estimation Methods
for In-Service Induction Motors. IEEE Transactions on Industry Applications. Vol. 42,
Issue 4, pp. 924-933
[4] Santos, V. S., Felipe, P. V., and Sarduy, J. G. (2013). Bacterial Foraging Algorithm Application
for Induction Motor Field Effi ciency Estimation Under Unbalanced Voltages. Measurement.
Vol. 46, Issue 7, pp. 2232-2237. DOI: 10.1016/j.measurement.2013.03.019
[5] Fogel, D. B. (2000). Evolutionary Computation: Toward a New Philosophy of Machine
Intelligence. Second ed. Piscataway, N. J. : IEEE Press.
[6] Chen, H., Zhu, Y., and Hu, K. (2011). Adaptive Bacterial Foraging Optimization. Abstract and
Applied Analysis. Vol. 2011, Article ID 108269. p. 27. DOI: 10.1155/2011/108269