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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
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    References listed on IDEAS

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    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).
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    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).

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