Result: An intelligent protection scheme for DC networks using a machine learning-based multi-agent platform.

Title:
An intelligent protection scheme for DC networks using a machine learning-based multi-agent platform.
Authors:
Esmaeilbeigi S; Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran. s_esmaeilbeigi@sbu.ac.ir., Kazemi Karegar H; Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran. h_kazemi@sbu.ac.ir., Akbarisharif A; Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran.
Source:
Scientific reports [Sci Rep] 2025 Sep 26; Vol. 15 (1), pp. 33124. Date of Electronic Publication: 2025 Sep 26.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
References:
Sensors (Basel). 2022 Dec 16;22(24):. (PMID: 36560301)
Contributed Indexing:
Keywords: DC microgrids; Decision tree; Deep neural networks; Fault detection; Fault location; Multi-agent-based protection; Support vector machine
Entry Date(s):
Date Created: 20250926 Latest Revision: 20250929
Update Code:
20250929
PubMed Central ID:
PMC12474987
DOI:
10.1038/s41598-025-17775-8
PMID:
41006419
Database:
MEDLINE

Further Information

The integration of DC networks including DC microgrids (DCMGs) into power systems is rapidly increasing. This is notably attributed to the distinctive fault current characteristics arising from inverter-based distributed generation resources in DCMGs, which differentiate them from conventional networks. As a result, the protection of DCMGs presents considerable challenges. Leveraging the recent strides in artificial intelligence, this paper introduces a novel multi-agent-based protection scheme for DC microgrids. Subsequently, three fault classification approaches are proposed in an intelligent protection scheme platform, employing diverse machine learning-based methods as a backup protection for fault detection and the main protection for fault location. The proposed protection scheme uses three main protection layers-namely, equipment, substation, and system-each endowed with specialized agents. In this way, the first and second fault classification approaches employ classifiers based on machine learning algorithms, such as Support Vector Machine (SVM) and Decision Tree (DT), to ascertain the microgrid status. Subsequent fault location is accomplished through various neural networks dedicated to the fault location. In the third approach, three Deep Neural Networks (DNNs) are proposed for fault classification, prompting the exclusion of classifiers from the substation layer due to the heightened training proficiency of DNNs. Intelligent Electronic Devices (IEDs) are placed at the beginning of the lines and send voltage and current information to the substation layer. Communication among agents and layers is performed by the IEC-61850 protocol. Comprehensive simulations and analyses are conducted using DIgSILENT, MATLAB, and Python (TensorFlow platform and Keras library) software. The findings underscore the efficacy of the proposed scheme and Fault Detection and Location (FDL) approaches, affirming their capability for precise fault classification and location determination.
(© 2025. The Author(s).)

Declarations. Competing interests: The authors declare no competing interests.