  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.
