A computationally low burden MPTC of induction machine without prediction loop and weighting factor

• Autorzy: Kiani Babak

Identyfikatory

DOI

10.24425/bpasts.2022.142050

• Języki publikacji: EN

Abstrakty

EN

This paper presents a novel method to overcome problems of finite set-model-based predictive torque control (MPTC) which has received a lot of attention in the last two decades. Tuning the weighting factor, evaluating a large number of switching states in the loop of the predictive control, and determining the duty cycle are three major challenges of the regular techniques. Torque and flux responses of deadbeat control have been developed to overcome these problems. In our method, firstly, the prediction stage is performed just once. Then, both the weighted cost function and its evaluation are replaced with only simple relationships. The relationships reduce torque ripple and THD of stator current compromisingly. In the next step, the length of the virtual vector is used to determine the duty cycle of the optimum voltage vector without any additional computations. The duty ratio does not focus on any relation or criteria minimizing torque or flux ripple. As a result, torque and flux ripples are reduced equally. The proposed duty cycle is calculated by using a predicted virtual voltage vector. Hence, no new computation is needed to determine the proposed duty cycle. Simulation and experimental results confirm both the steady and dynamic performance of the proposed method in all speed ranges.

Słowa kluczowe

EN

MPTC; model predictive torque control; induction motor; duty ratio; voltage vector; lookup table; weighting factor

PL

MPTC; modelowa predykcyjna kontrola momentu obrotowego; silnik indukcyjny; współczynnik wypełnienia impulsu; wektor napięcia; tabela przeglądowa; współczynnik ważenia

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2022
• Tom: Vol. 70, nr 4
• Strony: art. no. e142050
• Opis fizyczny: Bibliogr. 28 poz., rys., tab.

Twórcy

autor

Kiani Babak

• Department of Electrical Engineering, Izeh Branch, Islamic Azad University, Izeh, Iran

Bibliografia

• [1] D. Stando and M.P. Kazmierkowski, “Constant switching frequency predictive control scheme for three-level inverter-fed sensorless induction motor drive,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 5, pp. 1057–1068, 2020, doi: 10.24425/bpasts.2020.134668.
• [2] V. Talavat, S. Galvani, and M. Hajibeigy, “Direct predictive control of asynchronous machine torque using matrix converter,” Arch. Electr. Eng., vol. 67, no. 4, pp. 773–788, 2018, doi: 10.24425/aee.2018.124739.
• [3] R. Narayan and D.B. Subudhi, “Stator inter-turn fault detection of an induction motor using neuro-fuzzy techniques,” Arch. Control Sci., vol. 20, no. 3, pp. 363–376, 2010, doi: 10.2478/v10170-010-0022-7.
• [4] J. Rodriguez et al., “State of the art of finite control set model predictive control in power electronics,” IEEE Trans. Ind. Inform., vol. 9, no. 2, pp. 1003–1016, May 2013, doi: 10.1109/TII.2012.2221469.
• [5] S.A. Davari, D.A. Khaburi, and R. Kennel, “An improved FCSMPC algorithm for an induction motor with an imposed optimized weighting factor,” IEEE Trans. Power Electron., vol. 27, no. 3, pp. 1540–1551, Mar. 1981, doi: 10.1109/TPEL.2011.2162343.
• [6] T. Vyncke, S. Thielemans, T. Dierickx, R. Dewitte, M. Jacxsens, and J. Melkebeek, “Design choices for the prediction and optimization stage of finite-set model predictive control,” in Proc. 2011 Workshop on Predictive Control of Electrical Drives and Power Electronics, 2011, pp. 47–54, doi: 10.1109/PRECEDE.2011.6078687.
• [7] J. Rodriguez, R.M. Kennel, J.R. Espinoza, M. Trincado, C.A. Silva, and C.A. Rojas, “High performance control strategies for electrical drives: An experimental assessment,” IEEE Trans. Ind. Electron., vol. 59, no. 2, pp. 812–820, Feb. 2012, doi: 10.1109/TIE.2011.2158778.
• [8] C.A. Rojas, J. Rodriguez, F. Villarroel, J.R. Espinoza, C.A. Silva, and M. Trincado, “Predictive torque and flux control without weighting factors,” IEEE Trans. Ind. Electron., vol. 60, no. 2, pp. 680–690, Feb. 2013, doi: 10.1109/TIE.2012.2206344.
• [9] M. Norambuena, J. Rodriguez, Z. Zhang, F. Wang, C. Garcia, and R. Kennel, “A very simple strategy for high-quality performance of AC machines using model predictive control,” IEEE Trans. Power Electron., vol. 34, no. 1, pp. 794–800, Jan. 2019, doi: 10.1109/TPEL.2018.2812833.
• [10] Y. Zhang, B. Zhang, H. Yang, M. Norambuena, and M.J. Rodriguez, “Generalized sequential model predictive control of IM drives with field-weakening ability,” IEEE Trans. Power Electron., vol. 34, no. 9, pp. 8944–8955, Sept. 2019, doi: 10.1109/TPEL.2018.2886206.
• [11] M.R. Nikzad, B. Asaei, and S.O. Ahmadi. “Discrete duty-cycle-control method for direct torque control of induction motor drives with model predictive solution,” IEEE Trans. Power Electron., vol. 33, no. 3, pp. 2317–2329, Mar. 2018, doi: 10.1109/TPEL.2017.2690304.
• [12] F. Wang, S. Li, X. Mei, W. Xie, J. Rodríguez, and R. Kennel, “Model-based predictive direct control strategies for electrical drives: An experimental evaluation of PTC and PCC methods,” IEEE Trans. Ind. Inform., vol. 11, no. 3, pp. 671–681, Jun. 2015, doi: 10.1109/TII.2015.2423154.
• [13] Y. Zhang, H. Yang, and B. Xia, “Model-predictive control of induction motor drives: Torque control versus flux control,” IEEE Transactions on Ind. App., vol. 52, no. 5, pp. 4050–4060, Sep. 2016, doi: 10.1109/TIA.2016.2582796.
• [14] M. Habibullah, D.D. Lu, D. Xiao, and M.F. Rahman, “A simplified finite-state predictive direct torque control for induction motor drive,” IEEE Trans. Ind. Electron., vol. 63, no. 6, pp. 3964–3975, Jun. 2016, doi: 10.1109/TIE.2016.2519327.
• [15] W. Xie et al., “Finite-control-set model predictive torque control with a deadbeat solution for PMSM drives,” IEEE Trans. Ind. Electron., vol. 62, no. 9, pp. 5402–5410, Sep. 2015, doi: 10.1109/TIE.2015.2410767.
• [16] M. Mamdouh and M.A. Abido, “Efficient predictive torque control for induction motor drive,” IEEE Trans. Ind. Electron., vol. 69, no. 9, pp. 6757–6767, Sep. 2019, doi: 10.1109/TIE.2018.2879283.
• [17] Y. Zhang and J. Zhu, “Direct torque control of permanent magnet synchronous motor with a reduced torque ripple and commutation frequency,” IEEE Trans. Power Electron., vol. 26, no. 1, pp. 235–248, Jan. 2011, doi: 10.1109/TPEL.2010.2059047.
• [18] Y. Ren, Z.Q. Zhu, and J. Liu, ”Direct torque control of permanent-magnet synchronous machine drives with a simple duty ratio regulator,” IEEE Trans. Ind. Electron., vol. 61, no. 10, pp. 5249–5259, Oct. 2014, doi: 10.1109/TIE.2014.2300070.
• [19] Q. Liu and K. Hameyer, “Torque ripple minimization for direct torque control of PMSM with modified FSMPC,” IEEE Trans. Ind. Electron., vol. 52, no. 6, pp. 4855–4864, Dec. 2016, doi: 10.1109/TIA.2016.2599902.
• [20] L. Rovere, A. Formentini, A. Geaeta, P. Zanchetta, and M. Marchesoni, “Sensorless finite control set model predictive control for IPMSM drives,” IEEE Trans. Power Electron., vol. 63, no. 9, pp. 5921–5931, Sep. 2016, doi: 10.1109/TIE.2016.257 8281.
• [21] Y. Zhang, H. Yang, and B. Xia, “Model predictive torque control of induction motor drives with reduced torque ripple,” IET Electr. Power Appl., vol. 9, no. 9, pp. 595–604, 2015, doi: 10.1049/iet-epa.2015.0138.
• [22] Y. Zhang and H. Yang, “Model predictive flux control of induction motor drives with switching instant optimization,” IEEE Trans. Energy Convers., vol. 30, no. 3, pp. 1113–1122, Sep. 2015, doi: 10.1109/TEC.2015.2423692.
• [23] Y. Zhang and H. Yang, “Model predictive torque control of induction motor drives with optimal duty cycle control,” IEEE Trans. Power Electron., vol. 29, no. 12, pp. 6593–6603, Dec. 2014, doi: 10.1109/TPEL.2014.2302838.
• [24] Y. Zhang and H. Yang, “Generalized two-vector-based Model-predictive torque control of induction motor drives,” IEEE Trans. Power Electron., vol. 30, no. 7, pp. 3818–3829, July 2015, doi: 10.1109/TPEL.2014.2349508.
• [25] X. Wang and D. Sun, “Three-vector-based low-complexity model predictive direct power control strategy for doubly fed induction generators,” IEEE Trans. Power Electron., vol. 32, no. 1, pp. 773–782, Jan. 2017, doi: 10.1109/TPEL.2016.2532387.
• [26] S. Kang, J. Soh, and R. Kim, “Symmetrical three-vector-based model predictive control with deadbeat solution for IPMSM in rotating reference frame,” IEEE Trans. Ind. Electron., vol. 67, no. 1, pp. 159–168, Jan. 2020, doi: 10.1109/TIE.2018.2890490.
• [27] B. Kiani, B. Mozafari, S. Soleymani, and H. Mohammadnezhad Shourkaei, “Predictive torque control of induction motor drive with reduction of torque and flux ripple,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 4, p. e137727, 2021, doi: 10.24425/bpasts.2021.137727.
• [28] B. Kenny and R. Lorenz, “Stator- and rotor-flux-based deadbeat direct torque control of induction machines,” IEEE Trans. Ind. Appl., vol. 39, no. 4, pp. 1093–1101, Jul. 2003, doi: 10.1109/TIA.2003.813727.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).