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Finite Control Set–Model Predictive Control with Modulation to Mitigate Harmonic Component in Output Current for a Grid-Connected Inverter under Distorted Grid Conditions

Author

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  • Tien Hai Nguyen

    (Department of Electrical and Information Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea)

  • Kyeong-Hwa Kim

    (Department of Electrical and Information Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea)

Abstract

This paper presents an improved current control strategy for a three-phase grid-connected inverter under distorted grid conditions. In terms of performance, it is important for a grid-connected inverter to maintain the harmonic contents of inverter output currents below the specified limit even when the grid is subject to harmonic distortion. To address this problem, this paper proposes a modulated finite control set–model predictive control (FCS-MPC) scheme, which effectively mitigates the harmonic components in output current of a grid-connected inverter. In the proposed scheme, the system behavior in the future is predicted from the system model in the discrete-time domain. Then, the cost function is selected based on the control objective of system. This cost function is minimized during the optimization process to determine the control signals that minimize the cost function. In addition, since the proposed scheme requires pure sinusoidal reference currents in the stationary frame to work successfully, the moving average filter (MAF) is employed to enhance the performance of the traditional phase lock loop (PLL). Due to the control performance of the FCS-MPC scheme as well as the harmonic disturbance rejection capability of the MAF-PLL, the proposed scheme is able to suppress the harmonic distortion even in the presence of distorted grid condition, while retaining fast transient response. Comparative simulation results of different controllers verify the effectiveness of the proposed control scheme in compensating the harmonic disturbance. To validate the practical feasibility of the proposed scheme, the whole control algorithm is implemented on a 32-bit floating-point digital signal processor (DSP) TMS320F28335 to control a 2 kW three-phase grid-connected inverter. As a result, the proposed scheme is a promising approach toward improving the current quality of a grid-connected inverter under distorted grid conditions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:907-:d:103375
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    References listed on IDEAS

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    1. Bouzid, Allal M. & Guerrero, Josep M. & Cheriti, Ahmed & Bouhamida, Mohamed & Sicard, Pierre & Benghanem, Mustapha, 2015. "A survey on control of electric power distributed generation systems for microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 751-766.
    2. Ngoc Bao Lai & Kyeong-Hwa Kim, 2016. "An Improved Current Control Strategy for a Grid-Connected Inverter under Distorted Grid Conditions," Energies, MDPI, vol. 9(3), pages 1-23, March.
    3. Zhanfeng Song & Yanjun Tian & Zhe Chen & Yanting Hu, 2016. "Enhanced Predictive Current Control of Three-Phase Grid-Tied Reversible Converters with Improved Switching Patterns," Energies, MDPI, vol. 9(1), pages 1-16, January.
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    Cited by:

    1. Gimara Rajapakse & Shantha Jayasinghe & Alan Fleming & Michael Negnevitsky, 2017. "A Model Predictive Control-Based Power Converter System for Oscillating Water Column Wave Energy Converters," Energies, MDPI, vol. 10(10), pages 1-17, October.
    2. Ramon Guzmán & Luís García de Vicuña & Miguel Castilla & Jaume Miret & Antonio Camacho, 2017. "Finite Control Set Model Predictive Control for a Three-Phase Shunt Active Power Filter with a Kalman Filter-Based Estimation," Energies, MDPI, vol. 10(10), pages 1-14, October.
    3. Seung-Jin Yoon & Ngoc Bao Lai & Kyeong-Hwa Kim, 2018. "A Systematic Controller Design for a Grid-Connected Inverter with LCL Filter Using a Discrete-Time Integral State Feedback Control and State Observer," Energies, MDPI, vol. 11(2), pages 1-20, February.
    4. Yan Yang & Yeqin Wang & Weixing Zhang & Zhenghao Li & Rui Liang, 2022. "Design of Adaptive Fuzzy Sliding-Mode Control for High-Performance Islanded Inverter in Micro-Grid," Energies, MDPI, vol. 15(23), pages 1-25, December.
    5. Matthias Schiesser & Sébastien Wasterlain & Mario Marchesoni & Mauro Carpita, 2018. "A Simplified Design Strategy for Multi-Resonant Current Control of a Grid-Connected Voltage Source Inverter with an LCL Filter," Energies, MDPI, vol. 11(3), pages 1-15, March.
    6. Jaime A. Rohten & David N. Dewar & Pericle Zanchetta & Andrea Formentini & Javier A. Muñoz & Carlos R. Baier & José J. Silva, 2021. "Multivariable Deadbeat Control of Power Electronics Converters with Fast Dynamic Response and Fixed Switching Frequency," Energies, MDPI, vol. 14(2), pages 1-16, January.
    7. Azab, Mohamed, 2021. "A finite control set model predictive control scheme for single-phase grid-connected inverters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. 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.
    9. Angelo Lunardi & Eliomar R. Conde D & Jefferson de Assis & Darlan A. Fernandes & Alfeu J. Sguarezi Filho, 2021. "Model Predictive Control with Modulator Applied to Grid Inverter under Voltage Distorted," Energies, MDPI, vol. 14(16), pages 1-13, August.
    10. Cheng-Kai Lin & Jen-te Yu & Hao-Qun Huang & Jyun-Ting Wang & Hsing-Cheng Yu & Yen-Shin Lai, 2018. "A Dual-Voltage-Vector Model-Free Predictive Current Controller for Synchronous Reluctance Motor Drive Systems," Energies, MDPI, vol. 11(7), pages 1-29, July.

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