An Improved Big Bang-Big Crunch Algorithm for Estimating Three-Phase Induction Motors Efficiency

Document Type : Research paper

Authors

1 Islamic Azad University

2 ABB AG, Power Products Division

Abstract

Nowadays, the most generated electrical energy is consumed by three-phase induction motors. Thus, in order to carry out preventive measurements and maintenances and eventually employing high-efficiency motors, the efficiency evaluation of induction motors is vital. In this paper, a novel and efficient method based on Improved Big Bang-Big Crunch (I-BB-BC) Algorithm is presented for efficiency estimation in the induction motors. In order to estimate the induction motor’s efficiency, the measured current, the power factor and the input power are applied to the proposed method and an appropriate objective function is presented. The main advantage of the proposed method is efficiency evaluation of induction motor without any intrusive test. Moreover, a new effective and improved version of BB-BC algorithm is introduced. The presented modifications can improve the accuracy and speed of the classic version of algorithm. In order to demonstrate the capabilities of the proposed method, a comparison with other traditional methods and intelligent optimization algorithms is performed.

Keywords

Main Subjects


[1]              J. S. Hsu, J. D. Kueck, M. Olszewski, D. A. Casada and P. J. Otaduy, “Comparison of induction motor field efficiency evaluation methods,” IEEE Transactions on Industry Applications, vol. 34, no. 1, pp. 117-125, 1998.
[2]              B. Lu, T. G. Habetler and R. G. Harley, “A survey of efficiency-estimation methods for in-service induction motors,” IEEE Transactions on Industry Applications, vol. 42, no. 4, pp. 924-933, 2006.
[3]              C. S. Gajjar, J. M. Kinyua, M. A. Khan and P. S. Barendse, “Analysis of a non-intrusive efficiency estimation technique for induction machines compared to the IEEE 112B and IEC 34-2-1 standards,” IEEE Transactions on Industry Applications, vol. 51, no. 6, pp. 4541-4553, 2006.
[4]              M. Chirindo, M. A. Khan and P. S. Barendse, “Considerations for non-intrusive efficiency estimation of inverter-fed induction motors,” IEEE Transactions on Industrial Electronics, Early Access, Published Online, 2015.
[5]              IEEE standard test procedure for polyphase induction motors and generators, IEEE Standard 112, IEEE Power Engineering Society, New York, 1996.
[6]              B. Lu, T. G. Habetler and R. G. Harley, “A nonintrusive and in-service motor-efficiency estimation method using air-gap torque with considerations of condition monitoring,” IEEE Transactions on Industry Applications, vol. 44, no. 6, pp. 1666-1674, 2008.
[7]              Y. EI-Ibiary, “An accurate low cost method for determining electric motor’s efficiency for the purpose of plant energy management,” IEEE Transactions on Industry Applications, vol. 39, no. 4, pp. 12-19, 2003.
[8]              A. G. Siraki, P. Pillay and P. Angers, “Full load efficiency estimation of refurbished induction machines from no-load testing,” IEEE Transactions on Energy Conversion, vol. 28, no. 2, pp. 317-326, 2013.
[9]              M. Al-Badri, P. Pillay and P. Angers, “A novel algorithm for estimating refurbished three-phase induction motors efficiency using only no-load tests,” IEEE Transactions on Energy Conversion, vol. 30, no. 2, pp. 615-625, 2015.
[10]        V. Dlamini, R. Naidoo, M. Manyage, “A non-intrusive method for estimating motor efficiency using vibration signature analysis,” International Journal of Electrical Power and Energy Systems, vol. 45, no. 1, pp. 384-390, 2013.
[11]        J. R. Holmquist and M. A. Rooks, “Richter practical approach for determining motor efficiency in the field using calculated and measured values,” IEEE Transactions on Industry Applications, vol. 40, no. 1, pp. 242-248, 2004.
[12]        E. Babaei and N. Ghorbani, “Combined economic dispatch and reliability in power system by using PSO-SIF algorithm,” Journal of Operation and Automation in Power Engineering, vol. 3, no. 1, pp. 23-33, 2015.
[13]        M. Sedighizadeh and M. Mahmoodi “Optimal reconfiguration and capacitor allocation in radial distribution systems using the hybrid shuffled frog leaping algorithm in the fuzzy framework,” Journal of Operation and Automation in Power Engineering, vol. 3, no. 1, pp. 56-70, 2015.
[14]        T. Phumiphak and C. Chat-Uthai, “Estimation of induction motor parameters based on field test coupled with genetic algorithm,” in Proceedings of the IEEE International Conference on Power System Technology, pp. 1199- 1203, 2002.
[15]        A. Charette, J. Xu, A. Ba-Razzouk, P. Pillay and V. Rajagopalan, “The use of the genetic algorithm for in-situ efficiency measurement of an induction motor,” in Proceedings of the IEEE International Conference on Power Engineering Society, Winter Meeting, pp. 392-397, 2000.
[16]        M. Cunkas and T. Sag, “Efficiency determination of induction motors using multi-objective evolutionary algorithms,” Advances in Engineering Software, vol. 41, no. 2, pp. 255–261, 2010.
[17]        P. Nangsue, P. Pillay and S. E. Conry, “Evolutionary algorithms for induction motor parameter determination,” IEEE Transactions on Energy Conversion, vol. 14, no. 3, pp. 447-453, 1999.
[18]        B. Lu, C. Wenping, I. French, K. J. Bradley and T. G. Habetler, “Non-intrusive efficiency determination of in-service induction motors using genetic algorithm and air-gap torque methods,” in Proceedings of the IEEE 42nd IAS Annual Meeting, International Conference on Industry Applications, pp. 1186-1192, 2007.
[19]        M. Al-Badri, P. Pillay and P. Angers, “A novel in situ efficiency estimation algorithm for three-phase IM using GA, IEEE method F1 calculations, and pretested motor data,” IEEE Transactions on Energy Conversion, vol. 30, no. 3, pp. 1092-1102, 2015.
[20]        I. Kostov, V. Vasil Spasov and V. Rangelova, “Application of genetic algorithm for determining the parameters of induction motors,” Technical Gazette, vol. 16, no. 2, pp. 49-53, 2009.
[21]        V. P. Sakthivel and S. Subramanian, “On-site efficiency evaluation of three-phase induction motor based on particle swarm optimization,” Energy, vol. 36, no. 3, pp. 1713- 1720, 2011.
[22]        C. P. Salomon, C. Wilson, E. Luiz, G. Lambert, E. L. Bonaldi, E. L. Levy, J. G. Borges, “Motor efficiency evaluation using a new concept of stator resistance,” IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 11, pp. 2908-2917, 2015.
[23]        V. P. Sakthivel, R. Bhuvaneswari and S. Subramanian, “Non-intrusive efficiency 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, 2010.
[24]        V. S. Santos, P. R. Viego, J. R. Gomez, N. A. Lemozy, A. Jurado, E. C. Quispe, “Procedure for determining induction motor efficiency working under distorted grid voltages,” IEEE Transactions on Energy Conversion, vol. 30, no. 1, pp. 331-339, 2015.
[25]        V. S. Santos, P. V. Felipe and J. G. Sarduy, “Bacterial foraging algorithm application for induction motor field efficiency estimation under unbalanced voltages,” Measurement, vol. 46, no. 7, pp. 2232-2237, 2013.
[26]        V. P. Sakthivel, R. Bhuvaneswari and S. Subramanian, “An accurate and economical approach for induction motor field efficiency estimation using bacterial foraging algorithm,” Measurement, vol. 44, no. 4, pp. 674-684, 2011.
[27]        O. K. Erol and I. Eksin, “A new optimization method: big bang–big crunch,” Advances in Engineering Software, vol. 3, no. 7, pp. 106-111, 2006.
[28]        S. Sakthivel, S. A. Pandiyan, S. Marikani and S. K. Selvi, “Application of big bang big crunch algorithm for optimal power flow problems,” The International Journal of Engineering and Science, vol. 2, no. 4, pp. 41-47, 2013.
[29]        S. Sakthivel, M. Gayathri and V. Manimozhi, “A nature inspired optimization algorithm for reactive power control in a power system,” International Journal of Recent Technology and Engineering, vol. 2, no. 1, pp. 29-33, 2013.