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A Data-Driven Approach for Coherency Identification in Large-Scale Power Systems
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DOI：10.1109/tpwrs.2026.3703658.png)
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Abstract
En 中文
This paper proposes a temporal clustering–based approach for coherency identification in power grids that explicitly accounts for instrument transformer uncertainties and PMU measurement errors arising from communication failures. The method models the system using multiple single-bus–infinite-bus representations and introduces a metric for the electrical distance between physical system buses and a fictitious infinite bus, which is used to cluster buses into coherent groups. A key challenge of clustering-based methods—namely, the defining the clustering threshold—is systematically addressed by deriving a mathematically rigorous, power system–governed equation, resulting in a consistent method suitable for online applications. The effectiveness of the proposed data-driven approach is demonstrated using both simulated and real PMU signals, including scenarios with packet losses.
Keywords:
Electrical distance
transformer uncertainties
measurement errors
steady-state stability limit
graph
cut-set
PMU
Journal
IF:
7.2
Papers:
1.1W
Citations:
5.0W