IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i10p8018-d1147097.html
   My bibliography  Save this article

Machine Learning Supervisory Control of Grid-Forming Inverters in Islanded Mode

Author

Listed:
  • Hammed Olabisi Omotoso

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Abdullrahman A. Al-Shamma’a

    (Department of Electrical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia)

  • Mohammed Alharbi

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Hassan M. Hussein Farh

    (Department of Electrical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia)

  • Abdulaziz Alkuhayli

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Akram M. Abdurraqeeb

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Faisal Alsaif

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Umar Bawah

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Khaled E. Addoweesh

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

Abstract

This research paper presents a novel droop control strategy for sharing the load among three independent converter power systems in a microgrid. The proposed method employs a machine learning algorithm based on regression trees to regulate both the system frequency and terminal voltage at the point of common coupling (PCC). The aim is to ensure seamless transitions between different modes of operation and maintain the load demand while distributing it among the available sources. To validate the performance of the proposed approach, the paper compares it to a traditional proportional integral (PI) controller for controlling the dynamic response of the frequency and voltage at the PCC. The simulation experiments conducted in MATLAB/Simulink show the effectiveness of the regression tree machine learning algorithm over the PI controller, in terms of the step response and harmonic distortion of the system. The results of the study demonstrate that the proposed approach offers an improved stability and efficiency for the system, making it a promising solution for microgrid operations.

Suggested Citation

  • Hammed Olabisi Omotoso & Abdullrahman A. Al-Shamma’a & Mohammed Alharbi & Hassan M. Hussein Farh & Abdulaziz Alkuhayli & Akram M. Abdurraqeeb & Faisal Alsaif & Umar Bawah & Khaled E. Addoweesh, 2023. "Machine Learning Supervisory Control of Grid-Forming Inverters in Islanded Mode," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8018-:d:1147097
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/10/8018/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/10/8018/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Feng, Chen-Yu & Yang, Xiaodong & Afshan, Sahar & Irfan, Muhamamd, 2023. "Can renewable energy technology innovation promote mineral resources’ green utilization efficiency? Novel insights from regional development inequality," Resources Policy, Elsevier, vol. 82(C).
    2. Eslami, Ahmadreza & Negnevitsky, Michael & Franklin, Evan & Lyden, Sarah, 2022. "Review of AI applications in harmonic analysis in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    Full references (including those not matched with items on IDEAS)

    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. Li, Xinqiang & Wang, Cheng, 2024. "Clean Energy's influence on the mineral resource market in the ASEAN region," Resources Policy, Elsevier, vol. 91(C).
    2. Md Tariqul Islam & M. J. Hossain, 2023. "Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-33, February.
    3. Dong, Weiwei & Niu, XiaoQin & Nassani, Abdelmohsen A. & Naseem, Imran & Zaman, Khalid, 2024. "E-commerce mineral resource footprints: Investigating drivers for sustainable mining development," Resources Policy, Elsevier, vol. 89(C).
    4. Lin Wang & Yugang He & Renhong Wu, 2024. "Digitization Meets Energy Transition: Shaping the Future of Environmental Sustainability," Energies, MDPI, vol. 17(4), pages 1-25, February.
    5. Sun, Li & Li, Xiangyu & Wang, Yiting, 2024. "Digital trade growth and mineral resources In developing countries: Implications for green recovery," Resources Policy, Elsevier, vol. 88(C).
    6. Liu, Yiou & Yu, Yang & Huang, Yingfei & Guan, Weimin, 2024. "Utilizing the resources efficiency: Evidence from the impacts of media industry and digitalization," Resources Policy, Elsevier, vol. 88(C).
    7. Li, Yunfa & Liu, Fawei, 2023. "Assessment of impacts of land and electricity prices on mineral efficiency," Resources Policy, Elsevier, vol. 86(PB).
    8. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    9. Zhou, Xiaoxiao & Zhao, Yongan & Zhao, Xin & Xu, Junwei & Smutka, Luboš & Bilan, Yuriy, 2024. "Mineral resource drivers in the global south: A case study of Australia," Resources Policy, Elsevier, vol. 92(C).
    10. Li, Jun & He, Wei & Li, Guilong, 2024. "Natural resources, economic complexity and growth nexus: Role of digital governance in addressing the resource curse," Resources Policy, Elsevier, vol. 92(C).
    11. Qin Zhang & Jing Shao & Jianmin Qiao & Qian Cao & Haimeng Liu, 2024. "Coupling Relationships and Driving Mechanisms of Water–Energy–Food in China from the Perspective of Supply and Demand Security," Land, MDPI, vol. 13(10), pages 1-22, October.
    12. Wang, Wei & Liu, Dongsheng & Chen, Lifeng & Song, Meizhe & Xu, Jiaqi, 2024. "Assessment of impacts of green technology and innovation on sustainable mineral resource production," Resources Policy, Elsevier, vol. 90(C).
    13. Liu, Donghui & Shang, Zhaoyan & Lin, Tongzhi & Yue, Shuguang, 2024. "Balancing law, sustainability, and the economy in China's responsible mineral resource extraction path," Resources Policy, Elsevier, vol. 90(C).
    14. Cheng, Shixiong & Shu, Can & Jin, Meiling & He, Yuhang, 2023. "Balancing resources and sustainability: Analyzing the impact of mineral resources utilization on green growth," Resources Policy, Elsevier, vol. 86(PA).
    15. Shengzhe Ruan & Yi Song & Jinhua Cheng & Cheng Zhan, 2023. "Green Eco-Innovation and Supply of Critical Metals: Evidence from China," Sustainability, MDPI, vol. 15(17), pages 1-24, August.
    16. Manuela Panoiu & Caius Panoiu & Sergiu Mezinescu & Gabriel Militaru & Ioan Baciu, 2023. "Machine Learning Techniques Applied to the Harmonic Analysis of Railway Power Supply," Mathematics, MDPI, vol. 11(6), pages 1-20, March.
    17. Zhao, Congyu & Wang, Jianda & Dong, Kangyin & Wang, Kun, 2023. "How does renewable energy encourage carbon unlocking? A global case for decarbonization," Resources Policy, Elsevier, vol. 83(C).
    18. Li, Yinan & Huang, Yuxin, 2023. "Enhancing resources efficiency: Studying economic development in resource-rich regions for long-term sustainability of China," Resources Policy, Elsevier, vol. 86(PA).
    19. Shao, Yanmin & Li, Junlong & Zhang, Xueli, 2024. "Outward foreign direct investment and green technology innovation: A company and host country perspective," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    20. Hu, Qian & Gu, Yongkun, 2024. "Mineral resources and the green economy: A blueprint for sustainable development and innovation," Resources Policy, Elsevier, vol. 88(C).

    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:jsusta:v:15:y:2023:i:10:p:8018-:d:1147097. 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.