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Real-Time Implementation for Improvement of Weighting Coefficient Selection using Weighted Sum Method for Predictive Torque Control of PMSM Drive

  • Research Article-Electrical Engineering
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Abstract

In this article, selection of weighting coefficients is improved using the weighted sum method (WSM)-based objective function optimization for predictive torque control in PMSM drive under steady and dynamic conditions. Essentially, the empirical approaches used for weighting coefficient (WC) selection are intuitive, and the computation time is higher. In WSM approach, the error terms of stator flux and torque are obtained for each available state of the VSI. The attained dataset is normalized to 0–1 scaling, and then normalized error terms of both control parameters are combined into a single performance index for each switching state with suitable WCs. By maximizing this performance index, the optimal choice of switching state will be selected, with respect to priority given to control variables in objective function. Further, it results in reduced average switching frequency, %ripples (torque and flux), and stator current THD. The dSPACE-DS1104 R&D controller board is used for real-time implementation of conventional and proposed control algorithms under low speed, high speed, speed reversal, speed dynamics, and torque dynamics and also simulated in MATLAB/simulink environment. In order to verify the efficacy of PTC based on WSM, the hardware and simulation results were compared with conventional DTFC and CPTC strategies.

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References

  1. Eshwar, K.; Ravi Eswar, K.M.; Kumar, T.V.: Enhanced voltage switching states for direct torque controlled open end winding permanent magnet synchronous motor drive. Electric Power Compon. Syst. 48(9–10), 989–1005 (2022)

    Google Scholar 

  2. Pothuraju, R.; Tejavathu, R.; Panda, A.K.: multilevel inverter fed direct torque and flux control-space vector modulation of speed sensorless permanent magnet synchronous motor drive with improved steady state and dynamic characteristics. Int. J. Circuit Theory Appl. 50(4), 1279–1296 (2022)

    Article  Google Scholar 

  3. Dharmendra Kumar, P.; Ramesh, T.; Ramakrishna, P.: Performance Analysis of multi-level inverter-fed position sensorless PMSM drive using modified MPTC. IETE J. Res. (2021). https://doi.org/10.1080/03772063.2021.1999863

    Article  Google Scholar 

  4. Kusuma, E.; Eswar, K.M.R.; Kumar, T.V.: An effective predictive torque control scheme for PMSM drive without involvement of weighting factors. IEEE J. Emerg. Select. Top. Power Electron. 9(3), 2685–2697 (2020)

    Article  Google Scholar 

  5. Aihsan, M.Z.; Jidin, A.; Alias, A.; Tarusan, S.A.A.; Tahir, Z.M.; Sutikno, T.: Torque ripple minimization in direct torque control at low-speed operation using alternate switching technique. Int. J. Power Electron. Drive Syst. 13(1), 631 (2022)

    Google Scholar 

  6. Huang, L., Ji, J., Zhao, W., Tang, H., Tao, T., Jin, S.: Direct torque control for dual three-phase permanent magnet motor with improved torque and flux. IEEE Trans. Energy Convers. (2022)

  7. Vujji, A., Dahiya, R.: Experimental evaluation of VIKOR-based cost function optimization of finite control set-predictive torque control for permanent magnet synchronous motor drive. J. Failure Anal. Prevent., 1–16 (2022)

  8. Muddineni, V.P.; Bonala, A.K.; Sandepudi, S.R.: Grey relational analysis-based objective function optimization for predictive torque control of induction machine. IEEE Trans. Ind. Appl. 57(1), 835–844 (2021). https://doi.org/10.1109/TIA.2020.3037875

    Article  Google Scholar 

  9. Cortes, P., Kouro, S., La Rocca, B., Vargas, R., Rodriguez, J., Leon, J. I., Vazquez, S., Franquelo, L. G.: Guidelines for weighting factors design in Model Predictive Control of power converters and drives. In: IEEE International Conference on Industrial Technology, pp. 1–7, (2009).

  10. Kunisetti, V.P.K.; Meesala, R.E.K.; Thippiripati, V.K.: Improvised predictive torque control strategy for an open end winding induction motor drive fed with four-level inversion using normalized weighted sum model. IET Power Electron. 11(5), 808–816 (2018)

    Article  Google Scholar 

  11. Zanchetta, P.: Heuristic multi-objective optimization for cost function weights selection in finite states model predictive control. In: Proceedings under Workshop on Predictive Control of Electrical Drives and Power Electronics, pp. 70– 75 (2011)

  12. Villarroel, F., et al.: Multiobjective switching state selector for finite-states model predictive control based on fuzzy decision making in a matrix converter. IEEE Trans. Ind. Electron. 60(2), 589–599 (2013)

    Article  Google Scholar 

  13. Rojas, C.A.; Rodríguez, J.; Villarroel, F.; Espinoza, J.R.; Silva, C.A.; Trincado, M.: Predictive torque and flux control without weighting factors. IEEE Trans. Ind. Electron. 60(2), 681–690 (2013)

    Article  Google Scholar 

  14. Caseiro, L.M.A.; Mendes, A.M.S.; Cruz, S.M.A.: Dynamically weighted optimal switching vector model predictive control of power converters. IEEE Trans. Ind. Electron. 66(2), 1235–1245 (2019)

    Article  Google Scholar 

  15. Davari, S.A.; Khaburi, D.A.; Kennel, R.: An improved FCS —MPC algorithm for an induction motor with an imposed optimized weighting factor. IEEE Trans. Power Electron. 27(3), 1540–1551 (2012)

    Article  Google Scholar 

  16. Karamanakos, P.; Stolze, P.; Kennel, R.M.; Manias, S.; du Mouton, H.T.: Variable switching point predictive Toruqe control of induction machines. IEEE. J. Emerg. Sel. Top. Power Electron. 2(2), 285–295 (2014)

    Article  Google Scholar 

  17. Zhang, Y.; Yang, H.: Model-predictive flux control of induction motor drives with switching instant optimization. IEEE Trans. Energy Convers. 30(3), 1113–1122 (2015)

    Article  Google Scholar 

  18. Geyer, T.; Papafotiou, G.; Morari, M.: Model predictive direct torque control—part i: concept, algorithm, and analysis. IEEE Trans. Ind. Electron. 56(6), 1894–1905 (2009)

    Article  Google Scholar 

  19. Papafotiou, G.; Kley, J.; Papadopoulos, K.G.; Bohren, P.; Morari, M.: Model predictive direct torque control—part II: implementation and experimental evaluation. IEEE Trans. Ind. Electron. 56(6), 1906–1915 (2009)

    Article  Google Scholar 

  20. Triantaphyllou, E.; Shu, B.; Sanchez, S.N.; Ray, T.: Multi-criteria decision making: an operations research approach. Encycl. Electr. Electron. Eng. 15(1998), 175–186 (1998)

    Google Scholar 

  21. Tzeng, G.-H.; Huang, J.-J.: Multiple Attribute Decision Making Methods and Applications. CRC Press, Boca Raton (2011)

    Book  MATH  Google Scholar 

  22. Velasquez, M.; Hester, P.T.: An analysis of multi-criteria decision making methods. Int. J. Oper. Res. 10(2), 56–66 (2013)

    MathSciNet  Google Scholar 

  23. Jaberidoost, M.; Olfat, L.; Hosseini, A.; Kebriaeezadeh, A.; Abdollahi, M.; Alaeddini, M.; Dinarvand, R.: Pharmaceutical supply chain risk assessment in Iran using analytic hierarchy process (AHP) and simple additive weighting (SAW) methods. J. Pharm. Policy Pract. 8(9), 1–10 (2015)

    Google Scholar 

  24. Marler, R.T.; Arora, J.S.: The weighted sum method for multi-objective optimization: new insights. Struct. Multidiscip. Optim. 41(6), 853–862 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  25. Salih, Y.K.; See, O.H.; Ibrahim, R.W.; Yussof, S.; Iqbal, A.: A novel noncooperative game competing model using generalized simple additive weighting method to perform network selection in heterogenious wireless networks. Int. J. Commun. Syst. 28, 1112–1125 (2015)

    Article  Google Scholar 

  26. Tzeng, G.H.; Huang, J.J.: Multiple attribute decision making: methods and applications. CRC Press, Boca Raton (2011)

    Book  MATH  Google Scholar 

  27. Muddineni, V.P.; Sandepudi, S.R.; Bonala, A.K.: Finite control set predictive torque control for induction motor drive with simplified weighting factor selection using TOPSIS method. IET Electr. Power Appl. 11(5), 749–760 (2017)

    Article  Google Scholar 

  28. Muddineni, V.P.; Bonala, A.K.; Sandepudi, S.R.: Enhanced weighting factor selection for predictive torque control of induction motor drive based on VIKOR method. IET Electr. Power Appl. 10(9), 877–888 (2016)

    Article  Google Scholar 

  29. Hassan, A.A.; Kassem, A.M.: Modeling, simulation and performance improvements of a PMSM based on functional model predictive control. Arab. J. Sci. Eng. 38(11), 3071–3079 (2013)

    Article  MathSciNet  Google Scholar 

  30. Mamdouh, M.; Abido, M.A.; Hamouz, Z.: Weighting factor selection techniques for predictive torque control of induction motor drives: a comparison study. Arab. J. Sci. Eng. 43(2), 433–445 (2018)

    Article  Google Scholar 

  31. Muddineni, V.P.; Bonala, A.K.; Sandepudi, S.R.: Grey relational analysis-based objective function optimization for predictive torque control of induction machine. IEEE Trans. Ind. Appl. 57(1), 835–844 (2020)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology, Kurukshetra, Haryana, India

    Avinash Vujji & Ratna Dahiya

Authors
  1. Avinash Vujji
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  2. Ratna Dahiya
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Correspondence to Avinash Vujji.

Appendix A

Appendix A

Specifications of PMSM: DC-link voltage is 415 V, resistance of stator (Rs) is 1.12Ω, inductance of stator (Ls) is 10.5 mH, rated speed (Nr) is 1500 rpm, permanent magnet flux (φf) is 0.71Wb, 2-pole, 24 N m of rated torque, moment of inertia (J) is 0.0055 kg m2, and sampling time is 50μsec.

Specifications of voltage source inverter: voltage rating is 1200 V, current rating is 75A, and DC-link capacitor is 4700 µF.

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Vujji, A., Dahiya, R. Real-Time Implementation for Improvement of Weighting Coefficient Selection using Weighted Sum Method for Predictive Torque Control of PMSM Drive. Arab J Sci Eng 48, 6489–6505 (2023). https://doi.org/10.1007/s13369-022-07430-z

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  • DOI: https://doi.org/10.1007/s13369-022-07430-z

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