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591 results on '"Bozhko, Serhiy"'

24. Discontinuous Modulated Model Predictive Control for Low Inductance High-Speed Electric Drive Applications

Author

Aboelhassan, Ahmed, Wang, Shuo, Mikhaylov, Yury, Buticchi, Giampaolo, Galea, Michael, and Bozhko, Serhiy

Abstract

The performance improvement of high-power density electric machines with low inductance values for high-speed applications is still under investigation. The low inductance characteristics present higher harmonics and poor dynamic performance while applying the conventional control methods in the current loop. Modulated model predictive control (M2PC) with higher dynamics performance is a promising solution, which has been successfully applied to different applications including electric drive applications. However, the M2PC control of a machine with low inductance has not been fully investigated. Besides, the power converter operation can be improved through the implementation of certain switching patterns including discontinuous pulsewidth modulation (DPWM). So, a new strategy is proposed through the implementation of the M2PC controller based on the DPWM for low inductance electric machines. The presence of the modulator reduces the harmonic content while still maintaining the fast dynamic response of the M2PC. Also, the DPWM will provide higher voltage utilization, lower switching losses, and more potential to increase the switching frequency for a wide range of operating conditions. The proposed strategy has been applied successfully to a low inductance drive system as a case study with direct and quadrature axis inductances of 74 μH. The system has been tested and results have been demonstrated.

Published
2024
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25. Contributors

Author

Abdelhakim, Ahmed, primary, Al-Durra, Ahmed, additional, Artigas, José I., additional, Baguley, C.A., additional, Barragán, Luis A., additional, Blaabjerg, Frede, additional, Bozhko, Serhiy, additional, Burdío, José M., additional, Chen, Min, additional, Chen, Zhong, additional, Davari, Pooya, additional, Domínguez, Alberto, additional, Dorn-Gomba, Lea, additional, Dragičević, Tomislav, additional, Emadi, Ali, additional, Errouissi, Rachid, additional, Gao, Fei, additional, Guo, Jing, additional, Hou, Ruoyu, additional, Jayasinghe, S.G., additional, Ji, Bing, additional, Li, Yunwei, additional, Lucía, Óscar, additional, Ma, Ke, additional, Madawala, U.K., additional, Monmasson, Eric, additional, Muyeen, S.M., additional, Nademi, Hamed, additional, Nami, Alireza, additional, Navarro, Denis, additional, Pandžić, Hrvoje, additional, Saeedifard, Maryam, additional, Sarnago, Héctor, additional, Song, Yipeng, additional, Tan, Kun, additional, Urriza, Isidro, additional, Wheeler, Patrick, additional, Wu, Haimeng, additional, Xu, Dehong, additional, Xu, Dianguo, additional, Xu, Jun, additional, Yang, Tao, additional, Zare, Firuz, additional, Zhang, Ye, additional, Zhang, Donglai, additional, and Zhu, Ye, additional

Published
2018
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27. High-Fidelity Model Identification for Synchronous Reluctance Motor Drives

Author

Varvolik, Vasyl, Buticchi, Giampaolo, Wang, Shuo, Prystupa, Dmytro, Peresada, Sergei, Bozhko, Serhiy, and Galea, Michael

Abstract

This article presents an accurate model identification method for the synchronous reluctance machine (SynRel), considering the nonlinear magnetic behavior and spatial harmonics. The availability of the high-fidelity magnetic model is essential for high-performance control and modeling the vibro-acoustic behavior of the electric drive, allowing to predict not only the average torque but also the torque ripple behavior versus rotor position. The magnetic coenergy variation is calculated based on the flux linkages in order to yield the torque ripple. During the identification, the current disturbances introduced by the spatial effects and the inverter nonlinearities are properly suppressed by the merged proportional-integral (PI) controller with a repetitive technique. Besides, the inverter nonlinearity and digital control effects are carefully addressed to improve identification accuracy. The effectiveness of the proposed identification method is demonstrated by the comparison of the finite element (FE) simulation results and experiments for two SynRel prototypes.

Published
2023
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36. Mild Hybridization of Turboprop Engine With High-Power-Density Integrated Electric Drives

Author

Chen, Yuzheng, primary, Yang, Tao, additional, Khowja, Muhammad Raza, additional, Rocca, Antonino La, additional, Nasir, Usman, additional, Chowdhury, Shajjad, additional, Evans, Dean, additional, Kember, Dafydd, additional, Klonowski, Thomas, additional, Arnaud, Yohan, additional, Apostin, Lucie, additional, Liger, Thierry, additional, Cossart, Gregory, additional, Vakil, Gaurang, additional, Gerada, Chris, additional, Bozhko, Serhiy, additional, Detry, Sebastien, additional, Diette, Christophe, additional, and Wheeler, Patrick, additional

Published
2022
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44. Inverse application of artificial intelligence for the control of power converters

Author

Gao, Yuan, Wang, Songda, Hussaini, Habibu, Yang, Tao, Dragičević, Tomislav, Bozhko, Serhiy, Wheeler, Pat, Vazquez, Sergio, Gao, Yuan, Wang, Songda, Hussaini, Habibu, Yang, Tao, Dragičević, Tomislav, Bozhko, Serhiy, Wheeler, Pat, and Vazquez, Sergio

Abstract

This paper proposes a novel application method, Inverse Application of Artificial Intelligence (IAAI) for the control of power electronic converter systems. The proposed method can give the desired control coefficients/references in a simple way because, compared to conventional methods, IAAI only relies on a data-driven process with no need for an optimization process or substantial derivations. Noting that the IAAI approach uses artificial intelligence to provide feasible coefficients/references for the power converter control, rather than building a new controller. After illustrating the IAAI concept, a conventional application method of Artificial Neural Network (ANN) is discussed, an optimization-based design. Then, a two-source-converter microgrid case is studied to choose the best droop coefficients via the optimization-based approach. After that, the proposed IAAI method is employed for the same microgrid case to quickly find good droop coefficients. Furthermore, the IAAI method is applied to a modular multi-level converter (MMC) case, extending the MMC operation region under unbalanced grid faults. In the MMC case, both simulation and experimental online tests validate the operation, feasibility, and practicality of IAAI.

Published
2022

45. Inverse Application of Artificial Intelligence for the Control of Power Converters.

Author

Gao, Yuan, Wang, Songda, Hussaini, Habibu, Yang, Tao, Dragicevic, Tomislav, Bozhko, Serhiy, Wheeler, Patrick, and Vazquez, Sergio

Abstract

This article proposes a novel application method, inverse application of artificial intelligence (IAAI) for the control of power electronic converter systems. The proposed method can give the desired control coefficients/references in a simple way because, compared to conventional methods, IAAI only relies on a data-driven process with no need for an optimization process or substantial derivations. Noting that the IAAI approach uses artificial intelligence to provide feasible coefficients/references for the power converter control, rather than building a new controller. After illustrating the IAAI concept, a conventional application method of artificial neural network is discussed, an optimization-based design. Then, a two-source-converter microgrid case is studied to choose the best droop coefficients via the optimization-based approach. After that, the proposed IAAI method is employed for the same microgrid case to quickly find good droop coefficients. Furthermore, the IAAI method is applied to a modular multilevel converter (MMC) case, extending the MMC operation region under unbalanced grid faults. In the MMC case, both simulation and experimental online tests validate the operation, feasibility, and practicality of IAAI. [ABSTRACT FROM AUTHOR]

Published
2023
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