Comparative Study of Clustering Techniques for Real-Time Dynamic Model Reduction

TL;DR

This paper compares clustering techniques like graph clustering and k-means for real-time dynamic model reduction in power systems, aiming to improve computational efficiency amid increasing system complexity.

Contribution

It introduces the application of clustering methods to real-time power system data for dynamic model reduction, comparing their effectiveness with existing SVD-based approaches.

Findings

Clustering techniques can effectively identify generator groups with similar behavior.

Graph clustering and k-means outperform traditional SVD-based methods in certain scenarios.

Recommendations for practical implementation of clustering-based model reduction are provided.

Abstract

Dynamic model reduction in power systems is necessary for improving computational efficiency. Traditional model reduction using linearized models or online analysis is not adequate to capture dynamic behaviors of the power system, especially with the new mix of intermittent generation and intelligent consumption making the power system more dynamic and non-linear. Real-time dynamic model reduction has emerged to fill this important need. This paper explores using clustering techniques to analyze real-time phasor measurements to identify groups of generators with similar behavior, as well as a representative generator from each group for dynamic model reduction. Two clustering techniques -- graph clustering and k-means -- are considered. These techniques are compared with a previously developed dynamic model reduction approach using Singular Value Decomposition. Two sample power grid data sets are used to test these different model reduction techniques. Based on the algorithms' relative performance, recommendations are provided for practical use.