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Enhanced Fault Detection and Classification in AC Microgrids Through a Combination of Data Processing Techniques and Deep Neural Networks

Author

Listed:
  • Behrooz Taheri

    (Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin 34199-15195, Iran)

  • Seyed Amir Hosseini

    (Electrical and Computer Engineering Group, Golpayegan College of Engineering, Isfahan University of Technology, Golpayegan 87717-67498, Iran)

  • Hamed Hashemi-Dezaki

    (Department of Electrical and Computer Engineering, University of Kashan, 6 km Ghotbravandi Blvd., Kashan 87317-53135, Iran)

Abstract

This paper introduces an innovative method for the intelligent protection of AC microgrids that incorporate renewable energy sources and electric vehicle charging stations. To extract relevant features, current signals from both sides of the distribution line are sampled. Subsequently, the differential current is calculated, and the resultant signals are processed using Compressed Sensing Theory and Variational Mode Decomposition to extract key features. These extracted features serve as input data for training the proposed wide and deep learning model. The proposed method was evaluated on a microgrid that incorporated electric vehicle chargers and wind turbines. The results indicate that this approach can effectively identify and categorize different types of faults in AC microgrids. Moreover, it demonstrates stable and dependable performance in the face of typical transients, and its accuracy is not influenced by uncertainties in the microgrid topology.

Suggested Citation

  • Behrooz Taheri & Seyed Amir Hosseini & Hamed Hashemi-Dezaki, 2025. "Enhanced Fault Detection and Classification in AC Microgrids Through a Combination of Data Processing Techniques and Deep Neural Networks," Sustainability, MDPI, vol. 17(4), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1514-:d:1589507
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    References listed on IDEAS

    as
    1. Sirus Salehimehr & Seyed Mahdi Miraftabzadeh & Morris Brenna, 2024. "A Novel Machine Learning-Based Approach for Fault Detection and Location in Low-Voltage DC Microgrids," Sustainability, MDPI, vol. 16(7), pages 1-23, March.
    2. Noor Hussain & Mashood Nasir & Juan Carlos Vasquez & Josep M. Guerrero, 2020. "Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review," Energies, MDPI, vol. 13(9), pages 1-31, May.
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