Version 1
: Received: 19 June 2024 / Approved: 19 June 2024 / Online: 20 June 2024 (14:45:17 CEST)
How to cite:
SaeedKandezy, R.; Jiang, J. K-means++ for Critical Component Identification: Power Grid Case Study Using Measurement-Based Analysis. Preprints2024, 2024061383. https://doi.org/10.20944/preprints202406.1383.v1
SaeedKandezy, R.; Jiang, J. K-means++ for Critical Component Identification: Power Grid Case Study Using Measurement-Based Analysis. Preprints 2024, 2024061383. https://doi.org/10.20944/preprints202406.1383.v1
SaeedKandezy, R.; Jiang, J. K-means++ for Critical Component Identification: Power Grid Case Study Using Measurement-Based Analysis. Preprints2024, 2024061383. https://doi.org/10.20944/preprints202406.1383.v1
APA Style
SaeedKandezy, R., & Jiang, J. (2024). K-means++ for Critical Component Identification: Power Grid Case Study Using Measurement-Based Analysis. Preprints. https://doi.org/10.20944/preprints202406.1383.v1
Chicago/Turabian Style
SaeedKandezy, R. and John Jiang. 2024 "K-means++ for Critical Component Identification: Power Grid Case Study Using Measurement-Based Analysis" Preprints. https://doi.org/10.20944/preprints202406.1383.v1
Abstract
The inherent capabilities of the K-means++ algorithm to approximate system dynamics within complex systems are subjected by constructing a network structure that captures interconnections among identified components, extending its original purpose as a data clustering method and transforming it into a tool for systems analysis. Leveraging advanced measurement technologies and sophisticated data collection systems, the K-means++ algorithm unveils hidden relationships among components and identifies critical elements.
This study explored the algorithm's potential, facilitating the identification of critical components to enhance system operation, control, optimization, and decision-making and examining the practicality and resiliency of the method in real-world application with noisy and limited data. A case study conducted on power systems (IEEE 39-bus and IEEE 300-bus systems) exemplifies K-means++'s capacity to accurately identify critical components and approximate system dynamics, supported by performance metrics affirming its effectiveness and robustness in system analysis through measurements of bus similarity within clusters based on standard deviation and comparison of net tie-line flows in equivalent networks with the original network across scenarios.
Performance metrics, including the Silhouette Score, Davies-Bouldin Index, and Variation of Information (VI) score, further validated K-means++'s performance, yielding reliable and consistent results.
Keywords
Complex systems, Critical components, Dynamic identification, K-means++, Measurement-based, Network equivalence, Power systems, System analysis, System dynamics.
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
