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

Behrooz Taheri, Seyed Amir Hosseini () and Hamed Hashemi-Dezaki ()
Additional contact information
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

Sustainability, 2025, vol. 17, issue 4, 1-17

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.

Keywords: CST; deep neural network; EV; fault classification; fault detection; microgrid; signal processing; VMD; wind (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/4/1514/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/4/1514/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:4:p:1514-:d:1589507

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().