Nonintrusive Method for Induction Motor Equivalent Circuit Parameter Estimation using Chicken Swarm Optimization (CSO) Algorithm
Keywords:
Induction motor, Chicken Swarm Optimization, Parameter estimation, Equivalent circuit, Objective functionAbstract
This paper presents a nonintrusive method for estimating the parameters of an Induction Motor (IM) without the need for the conventional no-load and locked rotor tests. The method is based on a relatively new swarm-based algorithm called the Chicken Swarm Optimization (CSO). Two different equivalent circuits implementations have been considered for the parameter estimation scheme (one with parallel and the other with series magnetization circuit). The proposed parameter estimation method was validated experimentally on a standard 7.5 kW induction motor and the results were compared to those obtained using the IEEE Std. 112 reduced voltage impedance test method 3. The proposed CSO optimization method gave accurate estimates of the IM equivalent circuit parameters with maximum absolute errors of 5.4618% and 0.9285% for the parallel and series equivalent circuits representations respectively when compared to the IEEE Std. 112 results. However, standard deviation results in terms of the magnetization branch parameters, suggest that the series equivalent circuit model gives more repeatable results when compared to the parallel equivalent circuit.
References
Abdelhadi, B.; A. Benoudjit.; and N. Nait-Said (2005). Application of genetic algorithm with a novel adaptive scheme for the identification of induction machine parameters, IEEE Trans. Energy Convers., 20 (2): 284–291.
Al-badri, M., P. Pillay.; and P. Angers. (2015). A Novel Algorithm for Estimating Refurbished Three-Phase Induction Motors Efficiency, IEEE Trans. Energy Convers. 30 (2): 615–625.
Alturas, A. M.; S. M. Gadoue.; B. Zahawi.; and M. A. Elgendy. (2016). On the Identifiability of Steady-State Induction Machine Models Using External Measurements, Energy Conversion IEEE Transactions, 31(1), 251-259.
Bechouche, A.; H. Sediki.; D. O. Abdeslam.; and S. Haddad. (2012). A Novel Method for Identifying Parameters of Induction Motors at Standstill Using ADALINE, IEEE Trans. on Energy Convers, 27 (1): 105–116.
Carraro, M. and Zigliotto, M. (2014). Automatic Parameter Identification of Inverter-Fed Induction Motors at Standstill, IEEE Trans. on Ind. Elect., 61 (9): 4605–4613.
Castaldi, P. and Tilli, A. (2005). Parameter Estimation of Induction Motor at Standstill with Magnetic Flux Monitoring, IEEE Trans. on Cont. Sys. Tech. 13 (3): 386–400.
Cirrincione, M.; M. Pucci.; G. Cirrincione.; and G. A. Capolino. (2005). Constrained minimization for parameter estimation of induction motors in saturated and unsaturated conditions, IEEE Trans. Ind. Electron., 52 (5): 1391–1402.
Fleiter, T.; W. Eichhammer.; and K. J. Schleih. (2011). Energy efficiency in electric motor systems: Technical potentials and policy approaches for developing countries, United Nations Ind. Organ. Rep.: pp. 1–34.
Haque, M. H. (2008). Determination of NEMA design induction motor parameters from manufacturer data, IEEE Trans. Energy Convers., 23 ( 4): 997–1004.
IEEE Standard 112. (2017). IEEE Standard Test Procedure for Polyphase Induction motors and Generators.
Kanakoglu, A. I.; A. G. Yetgin.; H. Temurtas.; and M. Turan. (2014). Induction motor parameter estimation using metaheuristic methods, Turkish Journal of Electrical Engineering & Computer Sciences, 22: 1177-1192.
Lu, B.; W. Cao.; I. French.; K. J. Bradley.; and T. G. Habetler. (2007). Non-intrusive efficiency determination of in-service induction motors using genetic algorithm and air-gap torque methods, Conf. Rec. - IAS Annu. Meet. IEEE Ind. Appl. Soc.: 1186–1192.
Lu, B.; T. G. Habetler.; and R. G. Harley. (2008). A Nonintrusive and In-Service Motor-Efficiency Estimation Method Using Air-Gap Torque With Considerations of Condition Monitoring, IEEE Trans. on Ind. Applications, 44 (6): 1666–1674.
Meng, X.; Y. Liu.; and X. Gao. (2014). A new bio-inspired algorithm: chicken swarm optimization, In Springer, Advances in Swarm Intelligence, 8794: 86–94.
Mohan, N. (2012). Advanced Electric Drives: Analysis, Control, and Modelling using MATLAB/SIMULINK, John Wiley & Sons Inc., New Jersey (Chapters 2 & 3).
National Electrical Manufacturers Association (NEMA). (2006). NEMA MG 1-2006 for Motors and Gnerators.
Qu, C.; S. Zhao.; Y. Fu.; and W. He. (2017). Chicken Swarm Optimization Based on Elite Opposition-Based Learning, Mathematical Problems in Engineering, 2017, Hindawi, https://doi.org/10.1155/2017/2734362.
Ranta, M. and Hinkkanen, M. (2013). Online identification of parameters defining the saturation characteristics of induction machines, IEEE Trans. Ind. Appl., 49 (5): 2136–2145.
Reed D. M.; H. F. Hofmann.; and J. Sun. (2016). Offline Identification of Induction Machine Parameters With Core Loss Estimation Using the Stator Current Locus, IEEE Trans. on Energy Convers, 31 (4): 1549–1558.
Sandro C. L.; A. C. Carlos.; N. J. Wengerkievicz.; N. S. Batistela.; A. S. Pedro.; and Y. B. Anderson. (2017). Induction motor parameter estimation from manufacturer data using genetic algorithms and heuristic relationships, IEEE Power Electronics Conference (COBEP) 2017 Brazilian, 1-6, 2017.
Seesak, J. and Panthep, L. (2009). Parameter Estimation of Three-Phase Induction Motor by using Genetic Algorithm, Journal of Electrical Engineering & Technology 4 (3): 360-364.
Toliyat, H. A.; E. Levi; and M. Raina. (2003). A Review of RFO Induction Motor Parameter Estimation Techniques, IEEE Trans. on Energy Convers, 18 (2): 271–283.
Waide, P. and Brunner, C. U. (2011). Energy-Efficiency Policy Opportunities for Electric Motor-Driven Systems, Int. Energy Agency, Energy Effic. Ser. Rep.: pp. 1–132.
Wang, K.; J. Chiasson.; M. Bodson.; and L.M. Tolbert. (2004). A nonlinear least-squares approach for identification of the induction motor parameters, Decision and Control 2004. CDC. 43rd IEEE Conference on, 4, 3856-3861.
Wu, D.; F. Kong.; W. Gao.; Y. Shen.; and Z. Ji. (2015). Improved chicken swarm optimization, IEEE Int. Conf. Cyber Technol. Autom. Control Intell. Syst. IEEE-CYBER: 681–686.
Yang, S.; S. Yang.; Z. Xie.; M. Ma.; and X. Zhang. (2017). A new vector control strategy of induction motor based on iron loss model, Chinese Automation Congress (CAC) 2017, 3521-3526.
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