Implementation and proficiency analysis of enhanced graph algorithm for DC microgrid applications

Integrating renewable energy generation with the conventional grid supports reduces carbon emissions in the atmosphere. Despite technical advancements in protection strategies, critical issues concerning renewable integration in microgrid structures require standardized solutions. The essential aspe...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 14476 - 15
Main Authors: J, Mohamed Abdullah, V, Sumathi
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 24.06.2024
Nature Publishing Group
Nature Portfolio
Subjects:
ISSN:2045-2322, 2045-2322
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Integrating renewable energy generation with the conventional grid supports reduces carbon emissions in the atmosphere. Despite technical advancements in protection strategies, critical issues concerning renewable integration in microgrid structures require standardized solutions. The essential aspects that need to be concentrated during securing the grids are rapid fault interruption, false tripping and blinding of protection. This study proposes an innovative approach to enhance fault isolation speed through the implementation of a grid monitoring system (GMS) coupled with a fault identification method based on Kosaraju’s algorithm. This algorithm operates on the principles of overvoltage and overcurrent detection. The study assesses the efficacy of this approach by examining its integration with a Z-source circuit breaker and conducting tests on different fault types within a 13-bus system. Real-time simulations using Opal RT software are employed to experimentally validate the proposed methodology, ensuring its efficacy in fault interruption and isolation.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-65225-8