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Reduction of Prediction Errors for the Matrix Converter with an Improved Model Predictive Control

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  • Shuang Feng

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China)

  • Chaofan Wei

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China)

  • Jiaxing Lei

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China)

Abstract

In this paper, an improved model predictive control (MPC) is proposed for the matrix converter (MC). First, the conventional MPC which adopts the separately discretized prediction models is discussed. It shows that the conventional MPC ignores the input–output interaction in every sampling period. Consequently, additional prediction errors arise, resulting in more current harmonics. Second, the principle of the improved MPC is presented. With the interaction considered, the integral state-space equation of the whole MC system is constructed and discretized to obtain the precise model. The eigenvalue analysis shows that the proposed prediction model has the same eigenvalues with the continuous model, and thus is more accurate than the conventional one to describe the MC’s behavior in every sampling period. Finally, experimental results under various working conditions prove that the proposed approach can always increase the control accuracy and reduce the harmonic distortions, which in turn requires smaller filter components.

Suggested Citation

  • Shuang Feng & Chaofan Wei & Jiaxing Lei, 2019. "Reduction of Prediction Errors for the Matrix Converter with an Improved Model Predictive Control," Energies, MDPI, vol. 12(15), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:3029-:d:255164
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    References listed on IDEAS

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    1. Nguyen Dinh Tuyen & Phan Quoc Dzung, 2017. "Space Vector Modulation for an Indirect Matrix Converter with Improved Input Power Factor," Energies, MDPI, vol. 10(5), pages 1-13, April.
    2. Rutian Wang & Xue Wang & Chuang Liu & Xiwen Gao, 2018. "A Duty Cycle Space Vector Modulation Strategy for a Three-to-Five Phase Direct Matrix Converter," Energies, MDPI, vol. 11(2), pages 1-13, February.
    3. Jianwei Zhang & Margarita Norambuena & Li Li & David Dorrell & Jose Rodriguez, 2019. "Sequential Model Predictive Control of Three-Phase Direct Matrix Converter," Energies, MDPI, vol. 12(2), pages 1-14, January.
    4. Tingna Shi & Lingling Wu & Yan Yan & Changliang Xia, 2018. "Harmonic Spectrum of Output Voltage for Space Vector Pulse Width Modulated Ultra Sparse Matrix Converter," Energies, MDPI, vol. 11(2), pages 1-13, February.
    5. Weizhang Song & Jiang Liu & Xiangdong Sun & Fenjun Wu & Daqing Gao & Youyun Wang, 2018. "Research on Commutation and Coordination Control Strategy of Excitation Power Supply Based on Bidirectional Reduced Matrix Converter for Ion Accelerator," Energies, MDPI, vol. 11(12), pages 1-21, December.
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    Cited by:

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    2. Karol Wróbel & Piotr Serkies & Krzysztof Szabat, 2020. "Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches," Energies, MDPI, vol. 13(5), pages 1-15, March.
    3. Hui Yang & Rui Tu & Ke Wang & Jiaxing Lei & Wenjia Wang & Shuang Feng & Chaofan Wei, 2019. "A Hybrid Predictive Control for a Current Source Converter in an Aircraft DC Microgrid," Energies, MDPI, vol. 12(21), pages 1-14, October.
    4. Sergio Toledo & David Caballero & Edgar Maqueda & Juan J. Cáceres & Marco Rivera & Raúl Gregor & Patrick Wheeler, 2022. "Predictive Control Applied to Matrix Converters: A Systematic Literature Review," Energies, MDPI, vol. 15(20), pages 1-30, October.

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