# EFFICIENT PARAMETER ESTIMATION PROCEDURE USING SUNFLOWER OPTIMIZATION ALGORITHM FOR SIX-PHASE INDUCTION MOTOR

## Keywords:

Particle swarm optimization toll, Permanent magnet synchronous machines, Permanent magnet synchronous machines equivalent circuit, Online parameters estimation## Abstract

The accuracy of studying the performance of the six-phase induction motors (SPIMs) depends on the accurate estimation of the motor parameters. This article examines the performance evaluation of SPIMs among several optimization algorithms using parameter optimization. The competitive algorithms are differential evolution (DE), genetic algorithm (GA), Jaya optimization algorithm (JOA), particle swarm optimization (PSO), and sunflower optimization (SFO) algorithms. Parameter estimation is extracted from the performance curves based on manufacturer data. Laboratory verifications are performed on a SPIM modified from a three-phase induction motor. It also shows that using SFO gives convergence between measured and estimated parameters with small errors and fast response compared to many optimization algorithms. The statistical analysis of the results shows the effectiveness of the proposed SFO algorithm compared to other methods at different values of iterations.

## References

(1) M. Mechernene, A. Loucif, M. Zerikat, Induction motor control based on a fuzzy sliding mode approach, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 64, 1, pp. 39–44 (2019).

(2) D. Bhowmick, M. Manna, S.K. Chowdhury, Estimation of equivalent circuit parameters of transformer and induction motor from load data, IEEE Trans. Ind. Appl., 5, no. 3, pp. 2784–2791 (2018).

(3) M.I. Abdelwanis, R.A. El-Sehiemy, A fuzzy-based controller of a modified six-phase induction motor driving a pumping system, Iran. J. Sci. Technol. - Trans. Electr. Eng., 43, pp. 153–165 (2019).

(4) ***IEEE - Institute of Electrical and Electronics Engineers., Standard Test Procedure for Polyphase Induction Motors and Generators (ANSI), IEEE Std 112-2017, IEEE Stand. 112, pp. 3–5 (2017).

(5) G.K. Singh, Multi-phase induction machine drive research - A survey, Electr. Power Syst. Res., vol. 61, no. 2, pp. 139–147, 2002.

(6) Y.K. Damak, N.S. Kamoun, Estimation of asynchronous machine parameters and state variables, International Review on Modelling and Simulations, 4, 3, pp. 1112–1120 (2011).

(7) V.P. Sakthivel, R. Bhuvaneswari, S. Subramanian, Artificial immune system for parameter estimation of induction motor, Expert Syst. Appl., 37, 8, pp. 6109–6115 (2010),

(8) I. Perez, M. Gomez-Gonzalez, F. Jurado, Estimation of induction motor parameters using shuffled frog-leaping algorithm, Electr. Eng., 95, 3, pp. 267–275 (2012).

(9) G. Bucci G, F. Ciancetta, E. Fiorucci, A. Ometto, M.A. Segreto, A simplified indirect technique for the measurement of mechanical power in three-phase asynchronous motors, Int. J. Emerg. Electr. Power Syst., 20, 2 (2019).

(10) M.G. Bijan, P. Pillay, Efficiency estimation of the induction machine by particle swarm optimization using rapid test data with range constraints, IEEE Trans. Ind. Electron., 66, 8, pp. 5883–5894 (2019).

(11) J.J. Guedes, M.F. Castoldi, A. Goedtel, C.M. Agulhari, D.S. Sanches, Parameters estimation of three-phase induction motors using differential evolution, Electr. Power Syst. Res., 154, pp. 204–212 (2018).

(12) O.D. Che, E.L.H.S. Che, A.S. Abdel-Khalik, Parameter estimation of asymmetrical six-phase induction machines using modified standard tests, IEEE Trans. Ind. Electron., 64, 8, pp. 6075–6085 (2017).

(13) M.I. Abdelwanis, R.A. Sehiemy, M.A. Hamida, Hybrid optimization algorithm for parameter estimation of poly-phase induction motors with experimental verification, Energy AI, 5, p. 100083, Sep. 2021.

(14) E.G. Boudissa, M.H. HabbI, F. Gabour, M., Bounekhla, A new dynamic genetic selection algorithm: application to induction machine identification, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 66, 3, pp. 145–151 (2021).

(15) A.A. Abou El-Ela, R.A. El-Sehiemy, R.M. Rizk-Allah, D.A.Fatah, Solving multiobjective economical power dispatch problem using MO-FOA, in Twentieth International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, pp. 19–24 (2018).

(16) A.A. Elsakaan, R.A. El-Sehiemy, S.S. Kaddah, M.I. Elsaid, An enhanced moth-flame optimizer for solving non-smooth economic dispatch problems with emissions, Energy, 157, pp. 1063-1078 (2018),

(17) A.A. Abou El-Ela, R.A. El-Sehiemy, E.S. Ali, A. M. Kinawy, Minimisation of voltage fluctuation resulted from renewable energy sources uncertainty in distribution systems, IET Gener. Transm. Distrib., 13, 12, pp. 2339–2351 (2019).

(18) G.F. Gomes, S.S. da Cunha, A.C. Ancelotti, A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates, Eng. Comput., 35, 2, pp. 619–626 (2019).

(19) A.A.A. El-Ela, R.A. El-Sehiemy, A.S. Abbas, Optimal placement and sizing of distributed generation and capacitor banks in distribution systems using water cycle algorithm, IEEE Syst. J., 12, 4, pp. 3629-3633 (2018).

(20) A.M. Shaheen, R.A. El-Sehiemy, S.M. Farrag, A novel framework for power loss minimization by modified wind driven optimization algorithm, in Proceedings of 2018 International Conference on Innovative Trends in Computer Engineering, ITCE 2018, 2018-March, pp. 344–349 (2018).

(21) R.M. Rizk-Allah, H.M.A. Mageed, R.A. El-Sehiemy, S.H.E.A. Aleem, A. El Shahat, A new sine cosine optimization algorithm for solving combined non-convex economic and emission power dispatch problems, Int. J. Energy Convers., 5, 6, pp. 180-192 (2017).

(22) R.A. El-Sehiemy R.A. M.I. Abd-Elwanis, A.B. Kotb, Synchronous motor design using particle swarm optimization technique, in Proceedings of the 14th International Middle East Power Systems Conference (MEPCON’10), Cairo University, 2010, pp. 795–800.

(23) W. Deng, R. Yao, H. Zhao, X. Yang, G. Li, A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm, Soft Comput., 23, 7, pp. 2445–2462 (2019).

(24) A. Zangeneh, Optimal design of onshore wind farm collector system using particle swarm optimization and Prim’s algorithm, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 64, 4, pp. 349–356 (2019).

(25) L. Jia, X. Zhao, An Improved particle swarm optimization (PSO) optimized integral separation PID and its application on central position control system, IEEE Sens. J., 19, 16, pp. 7064–7071, (2019).

(26) R. Venkata Rao, Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems, Int. J. Ind. Eng. Comput., 7, 1, pp. 19–34 (2016).

(27) M. Mekki, A.A., Kansab, M. Matallah, M. Feliachi, Optimization of the inductor of an induction cooking system using particle swarm optimization method and fuzzy logic controller, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 65, 3–4, pp. 185–190 (2020).

(28) W. Hemly, M.A.E. Abbas, Optimal sizing of capacitor-bank types in the low voltage distribution networks using JAYA optimization, in 2018 9th International Renewable Energy Congress (IREC), Hammamet, Tunisia, 2018, pp. 1–5.

(29) Z. Yang, Y. Guo, Q. Niu, H. Ma, Y. Zhou, L. Zhang, A novel binary jaya optimization for economic/emission unit commitment, 2018 IEEE Congr. Evol. Comput. CEC 2018 - Proc., no. Jan. 2019 (2018).

(30) S. Mishra, P.K. Ray, Power quality improvement using photovoltaic fed DSTATCOM based on Jaya optimization, IEEE Trans. Sustain. Energy, 7, 4, pp. 1672–1680 (2016).

(31) S.P. Singh, T. Prakash, V.P. Singh, M.G. Babu, Analytic hierarchy process based automatic generation control of multi-area interconnected power system using Jaya algorithm, Eng. Appl. Artif. Intell., 60, no. September 2016, pp. 35–44, 2017.

(32) W. Warid, H. Hizam, N. Mariun, N. I. Abdul-Wahab, Optimal power flow using the Jaya algorithm, Energies, 9, 9, p. 678 (2016).

(33) I.N. Trivedi, S.N. Purohit, P. Jangir, M. Bhoye, Environment dispatch of distributed energy resources in a microgrid using Jaya algorithm, in 2016 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, India, 2016, pp. 224–228.

(34) P. Ocłoń et al., Thermal performance optimization of the underground power cable system by using a modified Jaya algorithm, Int. J. Therm. Sci., 123, pp. 162–180 (2018).

(35) A. F. Barakat, R. A. El-Sehiemy, M. I. Elsayd, E. Osman, Solving reactive power dispatch problem by using Jaya optimization algorithm, Int. J. Eng. Res. Africa, 36, pp. 12–24 (2018).

(36) D.P.V.S.V.S. Chilamkurthi, GC. Tirupatipati, J. Sulochanarani, V.K. Pamula, Design of optimal digital FIR filters using TLBO and Jaya algorithms, in 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, pp. 0538–0541 (2017).

(37) M.H. Qais, H.M. Hasanien, S. Alghuwainem, Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm, Appl. Energy, 205, pp. 109-117 (2019).

(38) Z. Yuan, W. Wang, H. Wang, N. Razmjooy, A new technique for optimal estimation of the circuit-based PEMFCs using developed Sunflower Optimization Algorithm, Energy Reports, 6, pp. 662–671 (2020).

(39) T.T. Nguyen, Enhanced sunflower optimization for placement distributed generation in distribution system, Int. J. Electr. Comput. Eng., 11, 1, pp. 107–113 (2021).

(40) T.L. Duong, T.T. Nguyen, Application of sunflower optimization algorithm for solving the security constrained optimal power flow problem, Eng. Technol. Appl. Sci. Res., 10, 3, pp. 5700–5705 (2020).

(41) R.A. El-Sehiemy, M.A. Hamida, T. Mesbahi, Parameter identification and state-of-charge estimation for lithium-polymer battery cells using enhanced sunflower optimization algorithm, Int. J. Hydrogen Energy, 45, 15, pp. 8833–8842 (2020).

(42) A.M. Shaheen, E.E. Elattar, R.A. El-Sehiemy, A. M. Elsayed, An Improved sunflower optimization algorithm-based Monte Carlo simulation for efficiency improvement of radial distribution systems considering wind power uncertainty, IEEE Access, 9, pp. 2332–2344 (2021).

(43) M.A.M. Shaheen, H.M. Hasanien, S.F. Mekhamer, H.E.A. Talaat, Optimal power flow of power systems including distributed generation units using sunflower optimization algorithm, IEEE Access, 7, pp. 109289–109300 (2019).

(44) A.M. Hussien, H.M. Hasanien, S.F. Mekhamer, Sunflower optimization algorithm-based optimal PI control for enhancing the performance of an autonomous operation of a microgrid, Shams Eng. J., 12, 2, pp. 1883–1893 (2021).

(45) C. Wang, Y. Liu, X. Liang, H. Guo, Y. Chen, Y. Zhao, Self-adaptive differential evolution algorithm with hybrid mutation operator for parameters identification of PMSM, Soft Comput., 22, 4, pp. 1263–1285 (2018).

(46 R.A. El-Sehiemy, M.A. El-Hosseini, A.E. Hassanien, Multiobjective real-coded genetic algorithm for economic/environmental dispatch problem, Stud. Informatics Control, 22, 2, pp. 113–122 (2013)

(47) E.A. Almabsout, R.A. El-Sehiemy, O.N.U. An, O. Bayat, A hybrid local search-genetic algorithm for simultaneous placement of DG units and shunt capacitors in radial distribution systems, IEEE Access, 8, pp. 54465-54481 (2020).

(48) F.B. Asmaa, A.E.S. Ragab, I.E. Mohamed, Close accord on particle swarm optimization variants for solving non-linear optimal reactive power dispatch problem, Int. J. Eng. Res. Africa, 46, pp. 88–105 (2020).

(49) R.A. El Sehiemy, F. Selim, B. Bentouati, M.A. Abido, A novel multi-objective hybrid particle swarm and salp optimization algorithm for technical-economical-environmental operation in power systems,” Energy, 193, 116817 (2020).