Data driven power flow analysis of SVC connected network during power network reconfiguration

Deblina Bhowmick and Dipu Sarkar ()
Additional contact information
Deblina Bhowmick: National Institute of Technology
Dipu Sarkar: National Institute of Technology

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 7, No 9, 2455-2466

Abstract: Abstract Blackouts in power systems have an impact on the nation’s economy and all aspects of human existence. The crucial buses or line’s fault led to cascading failures. In this concern Transmission network reconfiguration (TNR) has drawn a lot of interest. TNR is an essential action plan stage that allows for the restoration of power systems. This article proposes Machine Learning (ML) based power flow model which can be utilised for TNR. Further, the proposed model considers Static Var Compensator and different sectionalizing switches during different configuration. Three different system case studies have been carried out considering SVC connected without reconfiguration, reconfiguration without SVC, reconfiguration with SVC network power flow using KNN, Ridge Regression, Linear Regression ML approaches to determine the power flow output. The results of the proposed approach have been compared with the existing conventional load flow techniques. The simulation reveals the similar results which shows the effectiveness of the model. IEEE 30 bus system has been used as the test system.

Keywords: Power network; Network reconfiguration; SVC; Machine learning; FACTS device; Power flow (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-025-02790-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:ijsaem:v:16:y:2025:i:7:d:10.1007_s13198-025-02790-9

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-025-02790-9

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().