Hierarchical Clustering-Based Framework for Interconnected Power System Contingency Analysis

Bassam A. Hemad, Nader M. A. Ibrahim, Shereen A. Fayad and Hossam E. A. Talaat
Additional contact information
Bassam A. Hemad: Electrical Power System and Machines Dept., Suez University, Egypt
Nader M. A. Ibrahim: Electrical Power System and Machines Dept., Suez University, Egypt
Shereen A. Fayad: Electrical Power System and Machines Dept., Suez University, Egypt
Hossam E. A. Talaat: Electrical Engineering Dept., Future University in Egypt (FUE), Egypt

Energies, 2022, vol. 15, issue 15, 1-12

Abstract: This paper investigates a conceptual, theoretical framework for power system contingency analysis based on agglomerative hierarchical clustering. The security and integrity of modern power system networks have received considerable critical attention, and contingency analysis plays a vital role in assessing the adverse effects of losing a single element or more on the integrity of the power system network. However, the number of possible scenarios that should be investigated would be enormous, even for a small network. On the other hand, artificial intelligence (AI) techniques are well known for their remarkable ability to deal with massive data. Rapid developments in AI have led to a renewed interest in its applications in many power system studies over the last decades. Hence, this paper addresses the application of the hierarchical clustering algorithm supported by principal component analysis (PCA) for power system contingency screening and ranking. The study investigates the hierarchy clustering under different clustering numbers and similarity measures. The performance of the developed framework has been evaluated using the IEEE 24-bus test system. The simulation results show the effectiveness of the proposed framework for contingency analysis.

Keywords: cascading outage; contingency; steady-state security assessment; K-means algorithm; hierarchical clustering; PCA (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/15/5631/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/15/5631/ (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:jeners:v:15:y:2022:i:15:p:5631-:d:879194

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

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