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MPC with Constant Switching Frequency for Inverter-Based Distributed Generations in Microgrid Using Gradient Descent

Author

Listed:
  • Hyeong-Jun Yoo

    (Department of Electrical Engineering, Incheon National University, Songdo-dong, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea)

  • Thai-Thanh Nguyen

    (Department of Electrical Engineering, Incheon National University, Songdo-dong, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea)

  • Hak-Man Kim

    (Department of Electrical Engineering, Incheon National University, Songdo-dong, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea)

Abstract

Variable switching frequency in the finite control set model predictive control (FCS-MPC) method causes a negative impact on the converter efficiency and the design of the output filters. Several studies have addressed the problem, but they are either complicated or require heavy computation. This study proposes a new model predictive control (MPC) method with constant switching frequency, which is simple to implement and needs only a small computation time. The proposed MPC method is based on the gradient descent (GD) method to find the optimal voltage vector. Since the cost function of the MPC method is represented in the strongly convex function, the optimal voltage vector could be found quickly by using the GD method, which reduces the computation time of the MPC method. The design of the proposed MPC method based on GD (GD-MPC) is shown in this study. The feasibility of the proposed GD-MPC is evaluated in the real-time simulation using OPAL-RT technologies. The performance of the proposed method in the case of single inverter operation or parallel inverter operation is shown. A comparison study on the proposed GD-MPC and the MPC with the concept of the virtual state vector (VSV-MPC) is presented to demonstrate the effectiveness of the proposed predictive control. Real-time simulation results show that the proposed GD-MPC method performs better with a low total harmonic distortion (THD) value of output current and short computation time, compared to the VSV-MPC method.

Suggested Citation

  • Hyeong-Jun Yoo & Thai-Thanh Nguyen & Hak-Man Kim, 2019. "MPC with Constant Switching Frequency for Inverter-Based Distributed Generations in Microgrid Using Gradient Descent," Energies, MDPI, vol. 12(6), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1156-:d:217010
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    References listed on IDEAS

    as
    1. Thai-Thanh Nguyen & Hyeong-Jun Yoo & Hak-Man Kim, 2017. "Analyzing the Impacts of System Parameters on MPC-Based Frequency Control for a Stand-Alone Microgrid," Energies, MDPI, vol. 10(4), pages 1-17, March.
    2. Tien Hai Nguyen & Kyeong-Hwa Kim, 2017. "Finite Control Set–Model Predictive Control with Modulation to Mitigate Harmonic Component in Output Current for a Grid-Connected Inverter under Distorted Grid Conditions," Energies, MDPI, vol. 10(7), pages 1-25, July.
    3. Po Li & Ruiyu Li & Haifeng Feng, 2018. "Total Harmonic Distortion Oriented Finite Control Set Model Predictive Control for Single-Phase Inverters," Energies, MDPI, vol. 11(12), pages 1-15, December.
    4. Shiyang Hu & Guorong Liu & Nan Jin & Leilei Guo, 2018. "Constant-Frequency Model Predictive Direct Power Control for Fault-Tolerant Bidirectional Voltage-Source Converter with Balanced Capacitor Voltage," Energies, MDPI, vol. 11(10), pages 1-20, October.
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    Cited by:

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    2. Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
    3. Hoda Sorouri & Arman Oshnoei & Mateja Novak & Frede Blaabjerg & Amjad Anvari-Moghaddam, 2022. "Learning-Based Model Predictive Control of DC-DC Buck Converters in DC Microgrids: A Multi-Agent Deep Reinforcement Learning Approach," Energies, MDPI, vol. 15(15), pages 1-21, July.

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