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FPGA-Based Cost-Effective and Resource Optimized Solution of Predictive Direct Current Control for Power Converters

Author

Listed:
  • Deepa Sankar

    (Division of Electrical Engineering, Cochin University of Science and Technology, Ernakulam 682022, India)

  • Lakshmi Syamala

    (Department of Electronics and Communication Engineering, Mar Athanasius College of Engineering, Kothamangalam 686666, India)

  • Babu Chembathu Ayyappan

    (Division of Electrical Engineering, Cochin University of Science and Technology, Ernakulam 682022, India)

  • Mathew Kallarackal

    (Department of Electronics and Communication Engineering, Mar Athanasius College of Engineering, Kothamangalam 686666, India)

Abstract

Recent advances in power converter applications with highly demanding control goals require the efficient implementation of superior control strategies. However, the real-time application of such control strategies demands high computational power that necessitates efficient digital controllers like field programmable gate array (FPGA). The inherent parallelism offered by FPGAs minimizes the execution time and exhibits an excellent cost-performance trade-off. In addition, rapid advancements in FPGA technology with a broad portfolio of intellectual property (IP) cores, design tools, and robust embedded processors resulted in a design paradigm shift. This article proposes a low-cost solution for the resource-optimized implementation of dynamic, highly accurate, and computationally intensive finite state-predictive direct current control (FS-PDCC). The challenges for implementing complex control algorithms for power converters are discussed in detail, and the control is implemented in Intel’s low-cost non-volatile FPGA-MAX ® 10. An efficient design methodology using finite state machine (FSM) is adopted to achieve time/resource-efficient implementation. The parallel and pipelined architecture of FPGA provides better resource utilization with high execution speed. The experimental results prove the efficiency of FPGA-based cost-effective solutions that offer superior performance with better output quality.

Suggested Citation

  • Deepa Sankar & Lakshmi Syamala & Babu Chembathu Ayyappan & Mathew Kallarackal, 2021. "FPGA-Based Cost-Effective and Resource Optimized Solution of Predictive Direct Current Control for Power Converters," Energies, MDPI, vol. 14(22), pages 1-26, November.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7669-:d:680495
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    References listed on IDEAS

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    1. Vijay Kumar Singh & Ravi Nath Tripathi & Tsuyoshi Hanamoto, 2018. "HIL Co-Simulation of Finite Set-Model Predictive Control Using FPGA for a Three-Phase VSI System," Energies, MDPI, vol. 11(4), pages 1-15, April.
    2. Eduardo Zafra & Sergio Vazquez & Hipolito Guzman Miranda & Juan A. Sanchez & Abraham Marquez & Jose I. Leon & Leopoldo G. Franquelo, 2020. "Efficient FPSoC Prototyping of FCS-MPC for Three-Phase Voltage Source Inverters," Energies, MDPI, vol. 13(5), pages 1-16, March.
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    Cited by:

    1. Patryk Chaber & Andrzej Wojtulewicz, 2022. "Flexible Matrix of Controllers for Real Time Parallel Control," Energies, MDPI, vol. 15(5), pages 1-23, March.
    2. Victor Daniel Reyes Dreke & Mircea Lazar, 2022. "Long-Horizon Nonlinear Model Predictive Control of Modular Multilevel Converters," Energies, MDPI, vol. 15(4), pages 1-22, February.
    3. Mustafa Gokdag, 2022. "Modulated Predictive Control to Improve the Steady-State Performance of NSI-Based Electrification Systems," Energies, MDPI, vol. 15(6), pages 1-19, March.
    4. Nicholas D. de Andrade & Ruben B. Godoy & Edson A. Batista & Moacyr A. G. de Brito & Rafael L. R. Soares, 2022. "Embedded FPGA Controllers for Current Compensation Based on Modern Power Theories," Energies, MDPI, vol. 15(17), pages 1-17, August.
    5. Lakshmi Syamala & Deepa Sankar & Suhara Ekkarakkudy Makkar & Bos Mathew Jos & Mathew Kallarackal, 2022. "Hysteresis Based Quasi Fixed Frequency Current Control of Single Phase Full Bridge Grid Integrated Voltage Source Inverter," Energies, MDPI, vol. 15(21), pages 1-17, October.

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