IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i6p1156-d217010.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/6/1156/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/6/1156/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adile Akpunar & Serdar Iplikci, 2020. "Runge-Kutta Model Predictive Speed Control for Permanent Magnet Synchronous Motors," Energies, MDPI, vol. 13(5), pages 1-17, March.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Andrés Henao-Muñoz & Andrés Saavedra-Montes & Carlos Ramos-Paja, 2018. "Optimal Power Dispatch of Small-Scale Standalone Microgrid Located in Colombian Territory," Energies, MDPI, vol. 11(7), pages 1-20, July.
    3. Andrea Bonfiglio & Massimo Brignone & Marco Invernizzi & Alessandro Labella & Daniele Mestriner & Renato Procopio, 2017. "A Simplified Microgrid Model for the Validation of Islanded Control Logics," Energies, MDPI, vol. 10(8), pages 1-28, August.
    4. 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.
    5. Byung-Ki Jeon & Eui-Jong Kim, 2021. "LSTM-Based Model Predictive Control for Optimal Temperature Set-Point Planning," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Feras Alasali & Stephen Haben & Victor Becerra & William Holderbaum, 2017. "Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems," Energies, MDPI, vol. 10(10), pages 1-18, October.
    11. Byung-Ki Jeon & Eui-Jong Kim & Younggy Shin & Kyoung-Ho Lee, 2018. "Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems," Sustainability, MDPI, vol. 11(1), pages 1-16, December.
    12. Tiago Lukasievicz & Ricardo Oliveira & César Torrico, 2018. "A Control Approach and Supplementary Controllers for a Stand-Alone System with Predominance of Wind Generation," Energies, MDPI, vol. 11(2), pages 1-17, February.
    13. 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.
    14. Guo-Qiang Zeng & Xiao-Qing Xie & Min-Rong Chen, 2017. "An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations," Energies, MDPI, vol. 10(11), pages 1-23, November.
    15. Min-Rong Chen & Guo-Qiang Zeng & Yu-Xing Dai & Kang-Di Lu & Da-Qiang Bi, 2018. "Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid," Energies, MDPI, vol. 12(1), pages 1-21, December.
    16. Lynn Verkroost & Joachim Druant & Hendrik Vansompel & Frederik De Belie & Peter Sergeant, 2019. "Performance Degradation of Surface PMSMs with Demagnetization Defect under Predictive Current Control," Energies, MDPI, vol. 12(5), pages 1-20, February.
    17. José Antonio Cortajarena & Oscar Barambones & Patxi Alkorta & Jon Cortajarena, 2021. "Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid," Mathematics, MDPI, vol. 9(2), pages 1-18, January.
    18. Rui Qin & Chunhua Yang & Hongwei Tao & Tao Peng & Chao Yang & Zhiwen Chen, 2019. "A Power Loss Decrease Method Based on Finite Set Model Predictive Control for a Motor Emulator with Reduced Switch Count," Energies, MDPI, vol. 12(24), pages 1-25, December.
    19. Yajing Gao & Fushen Xue & Wenhai Yang & Yanping Sun & Yongjian Sun & Haifeng Liang & Peng Li, 2017. "A Three-Part Electricity Price Mechanism for Photovoltaic-Battery Energy Storage Power Plants Considering the Power Quality and Ancillary Service," Energies, MDPI, vol. 10(9), pages 1-21, August.
    20. Byung-ki Jeon & Eui-Jong Kim, 2020. "Next-Day Prediction of Hourly Solar Irradiance Using Local Weather Forecasts and LSTM Trained with Non-Local Data," Energies, MDPI, vol. 13(20), pages 1-16, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1156-:d:217010. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.