Energy-efficient predictive control for field-orientation induction machine drives
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- Category: Content №6 2020
- Last Updated on 22 December 2020
- Published on 30 November -0001
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Authors:
G.G.Diachenko, orcid.org/0000-0001-9105-1951, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
G.Schullerus, orcid.org/0000-0001-9740-9213, Reutlingen University, Reutlingen, Germany, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
A.Dominic, orcid.org/0000-0001-6872-1814, Reutlingen University, Reutlingen, Germany, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
O.O.Aziukovskyi, orcid.org/0000-0003-1901-4333, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2020, (6): 061 - 067
https://doi.org/10.33271/nvngu/2020-6/061
Abstract:
Purpose. To improve the efficiency of the closed-cycle operation of the field-orientation induction machine in dynamic behavior when load conditions are changing, considering the nonlinearities of the main inductance.
Methodology. The optimal control problem is defined as the minimization of the time integral of the energy losses. The algorithm observed in this paper uses the Matlab/Simulink, dSPACE real-time interface, and C language. Handling real-time applications is made in ControlDesk experiment software for seamless ECU development.
Findings. Adiscrete-time model with an integrated predictive control scheme where the optimization is performed online at every sampling step has been developed. The optimal field-producing current trajectory is determined, so that the copper losses are minimized over a wide operational range. Additionally, the comparison of measurement results with conventional methods is provided, which validates the advantages and performance of the control scheme.
Originality. To solve the given problem, the information vector on the current state of the coordinates of the electromechanical system is used to form a controlling influence in the dynamic mode of operation. For the first time, the formation process of controls has considered the current state and the desired future state of the system in the real-time domain.
Practical value. Apredictive iterative approach for optimal flux level of an induction machine is important to generate the required electromagnetic torque and to reduce power losses simultaneously.
Keywords: predictive control, energy efficiency, dynamic operation, real-time implementation
References.
1. Beshta, A., Beshta, A., Balakhontsev, A., & Khudolii, S. (2019). Performances of asynchronous motor within variable frequency drive with additional power source plugged via combined converter. 2019 IEEE 6th International Conference on Energy Smart Systems (ESS), (pp. 156-160). Kyiv, Ukraine. https://doi.org/10.1109/ESS.2019.8764192.
2. Koriashkina, L.S., Deryugin, O.V., Fedoriachenko, S.O., Cheberiachko, S.I., & Vesela, M.A. (2019). On determining productive capacity of EV traction battery repair area. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (5), 113-121. https://doi.org/10.29202/nvngu/2019-5/17.
3. Beshta, O.S., Fedoreiko, V.S., Balakhontsev, O.V., & Khudolii, S.S. (2013). Dependence of electric drives thermal state on its operation mode. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (6), 67-72.
4. Hannan, M.A., Ali, J.A., Mohamed, A., & Hussain, A. (2018). Optimization techniques to enhance the performance of induction motor drives: A review. Renewable and Sustainable Energy Reviews, 81, 1611-1626. https://doi.org/10.1016/j.rser.2017.05.240.
5. Diachenko, G., & Schullerus, G. (2015). Simple dynamic energy efficient field oriented control in induction motors. Proceedings of the 18th International Symposium on Power Electronics EE2015, (pp. 1-5). Novi Sad, Serbia. Retrieved from https://elprivod.nmu.org.ua/ua/articles/_Session8-03615.pdf.
6. Diachenko, G., Aziukovskyi, O., Rogoza, M., & Yakimets,S. (2019). Optimal field-oriented control of an induction motor for loss minimization in dynamic operation. 2019 IEEE International Conference on Modern Electrical and Energy Systems (MEES), (pp. 94-97). Kremenchuk, Ukraine. https://doi.org/10.1109/MEES.2019.8896455.
7. Stumper, J., Dtlinger, A., & Kennel, R. (2013). Loss minimization of induction machines in dynamic operation. IEEE Transactions on Energy Conversion, 28(3), 726-735. https://doi.org/10.1109/TEC.2013.2262048.
8. Borisevich, A., & Schullerus, G. (2016). Energy efficient control of an induction machine under torque step changes. IEEE Transactions on Energy Conversion, 31(4), 1295-1303. https://doi.org/10.1109/TEC.2016.2561307.
9. Abdelati, R., & Mimouni, M.F. (2019). Optimal control strategy of an induction motor for loss minimization using Pontryaguin principle. European Journal of Control, 49, 94-106. https://doi.org/10.1016/j.ejcon.2019.02.004.
10. Kpernick, B., & Graichen, K. (2014). The gradient based nonlinear model predictive control software GRAMPC. 2014 European Control Conference (ECC), (pp. 1170-1175). Strasbourg, France. https://doi.org/10.1109/ecc.2014.6862353.
11. Diachenko, G. (2020). Rotor flux controller for induction machines considering main inductance saturation. Problems of the regional energetics, 3(47), 10-19. https://doi.org/10.5281/zenodo.4018933.
12. Dominic, A., Schullerus, G., & Winter, M. (2019). Optimal flux and current trajectories for efficient operation of induction machines. 2019 20th International Symposium on Power Electronics (Ee), (pp. 1-6). Novi Sad, Serbia. https://doi.org/10.1109/PEE.2019.8923512.
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