IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i24p9397-d1001031.html
   My bibliography  Save this article

Machine Learning Based Protection Scheme for Low Voltage AC Microgrids

Author

Listed:
  • Muhammad Uzair

    (School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia)

  • Mohsen Eskandari

    (School of Electrical Engineering and Telecommunication, University of New South Wales, Sydney, NSW 2052, Australia)

  • Li Li

    (School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia)

  • Jianguo Zhu

    (School of Electrical and Information Engineering, The University of Sydney, Camperdown, NSW 2006, Australia)

Abstract

The microgrid (MG) is a popular concept to handle the high penetration of distributed energy resources, such as renewable and energy storage systems, into electric grids. However, the integration of inverter-interfaced distributed generation units (IIDGs) imposes control and protection challenges. Fault identification, classification and isolation are major concerns with IIDGs-based active MGs where IIDGs reveal arbitrary impedance and thus different fault characteristics. Moreover, bidirectional complex power flow creates extra difficulties for fault analysis. This makes the conventional methods inefficient, and a new paradigm in protection schemes is needed for IIDGs-dominated MGs. In this paper, a machine-learning (ML)-based protection technique is developed for IIDG-based AC MGs by extracting unique and novel features for detecting and classifying symmetrical and unsymmetrical faults. Different signals, namely, 400 samples, for wide variations in operating conditions of an MG are obtained through electromagnetic transient simulations in DIgSILENT PowerFactory. After retrieving and pre-processing the signals, 10 different feature extraction techniques, including new peaks metric and max factor, are applied to obtain 100 features. They are ranked using the Kruskal–Wallis H-Test to identify the best performing features, apart from estimating predictor importance for ensemble ML classification. The top 18 features are used as input to train 35 classification learners. Random Forest (RF) outperformed all other ML classifiers for fault detection and fault type classification with faulted phase identification. Compared to previous methods, the results show better performance of the proposed method.

Suggested Citation

  • Muhammad Uzair & Mohsen Eskandari & Li Li & Jianguo Zhu, 2022. "Machine Learning Based Protection Scheme for Low Voltage AC Microgrids," Energies, MDPI, vol. 15(24), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9397-:d:1001031
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/24/9397/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/24/9397/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alireza Forouzesh & Mohammad S. Golsorkhi & Mehdi Savaghebi & Mehdi Baharizadeh, 2021. "Support Vector Machine Based Fault Location Identification in Microgrids Using Interharmonic Injection," Energies, MDPI, vol. 14(8), pages 1-14, April.
    2. Patnaik, Bhaskar & Mishra, Manohar & Bansal, Ramesh C. & Jena, Ranjan K., 2021. "MODWT-XGBoost based smart energy solution for fault detection and classification in a smart microgrid," Applied Energy, Elsevier, vol. 285(C).
    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. Alireza Gorjian & Mohsen Eskandari & Mohammad H. Moradi, 2023. "Conservation Voltage Reduction in Modern Power Systems: Applications, Implementation, Quantification, and AI-Assisted Techniques," Energies, MDPI, vol. 16(5), pages 1-36, March.
    2. Zeyue Sun & Mohsen Eskandari & Chaoran Zheng & Ming Li, 2022. "Handling Computation Hardness and Time Complexity Issue of Battery Energy Storage Scheduling in Microgrids by Deep Reinforcement Learning," Energies, MDPI, vol. 16(1), pages 1-20, December.
    3. Uzair, Muhammad & Li, Li & Eskandari, Mohsen & Hossain, Jahangir & Zhu, Jian Guo, 2023. "Challenges, advances and future trends in AC microgrid protection: With a focus on intelligent learning methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).

    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. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    2. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    3. Faisal Mumtaz & Haseeb Hassan Khan & Amad Zafar & Muhammad Umair Ali & Kashif Imran, 2022. "A State-Observer-Based Protection Scheme for AC Microgrids with Recurrent Neural Network Assistance," Energies, MDPI, vol. 15(22), pages 1-22, November.
    4. Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Zhao, Guanjia & Ma, Suxia, 2023. "Data-driven modeling-based digital twin of supercritical coal-fired boiler for metal temperature anomaly detection," Energy, Elsevier, vol. 278(PA).
    5. Abulanwar, Sayed & Ghanem, Abdelhady & Rizk, Mohammad E.M. & Hu, Weihao, 2021. "Adaptive synergistic control strategy for a hybrid AC/DC microgrid during normal operation and contingencies," Applied Energy, Elsevier, vol. 304(C).
    6. Abha Pragati & Manohar Mishra & Pravat Kumar Rout & Debadatta Amaresh Gadanayak & Shazia Hasan & B. Rajanarayan Prusty, 2023. "A Comprehensive Survey of HVDC Protection System: Fault Analysis, Methodology, Issues, Challenges, and Future Perspective," Energies, MDPI, vol. 16(11), pages 1-39, May.
    7. Aparna Kumari & Rushil Kaushikkumar Patel & Urvi Chintukumar Sukharamwala & Sudeep Tanwar & Maria Simona Raboaca & Aldosary Saad & Amr Tolba, 2022. "AI-Empowered Attack Detection and Prevention Scheme for Smart Grid System," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    8. Yin, Linfei & Wu, Yunzhi, 2022. "Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy," Applied Energy, Elsevier, vol. 307(C).
    9. Hamed Rezapour & Sadegh Jamali & Alireza Bahmanyar, 2023. "Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks," Energies, MDPI, vol. 16(12), pages 1-18, June.
    10. Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Liu, Jiao & Yu, Daren, 2021. "Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers," Applied Energy, Elsevier, vol. 302(C).
    11. Sun, Jian & Liu, Gang & Sun, Boyang & Xiao, Gang, 2021. "Light-stacking strengthened fusion based building energy consumption prediction framework via variable weight feature selection," Applied Energy, Elsevier, vol. 303(C).
    12. Peng, Jieyang & Kimmig, Andreas & Niu, Zhibin & Wang, Jiahai & Liu, Xiufeng & Ovtcharova, Jivka, 2021. "A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework," Applied Energy, Elsevier, vol. 299(C).
    13. Younis M. Nsaif & Molla Shahadat Hossain Lipu & Aini Hussain & Afida Ayob & Yushaizad Yusof & Muhammad Ammirrul A. M. Zainuri, 2022. "A New Voltage Based Fault Detection Technique for Distribution Network Connected to Photovoltaic Sources Using Variational Mode Decomposition Integrated Ensemble Bagged Trees Approach," Energies, MDPI, vol. 15(20), pages 1-20, October.
    14. Li, Yiying & Yan, Cheng & Ren, Xiaohang, 2023. "Do uncertainties affect clean energy markets? Comparisons from a multi-frequency and multi-quantile framework," Energy Economics, Elsevier, vol. 121(C).
    15. Katherine Cabana-Jiménez & John E. Candelo-Becerra & Vladimir Sousa Santos, 2022. "Comprehensive Analysis of Microgrids Configurations and Topologies," Sustainability, MDPI, vol. 14(3), pages 1-25, January.

    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:15:y:2022:i:24:p:9397-:d:1001031. 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.