  The dynamic mode decomposition (DMD) technique extracts the dominant modes
characterizing the innate dynamical behavior of the system within the
measurement data. For appropriate identification of dominant modes from the
measurement data, the DMD algorithm necessitates ensuring the quality of the
input measurement data sequences. On that account, for validating the usability
of the dataset for the DMD algorithm, the paper proposed two conditions:
Persistence of excitation (PE) and the Granger Causality Test (GCT). The
virtual data sequences are designed with the hankel matrix representation such
that the dimensions of the subspace spanning the essential system modes are
increased with the addition of new state variables. The PE condition provides
the lower bound for the trajectory length, and the GCT provides the order of
the model. Satisfying the PE condition enables estimating an approximate linear
model, but the predictability with the identified model is only assured with
the temporal causation among data searched with GCT. The proposed methodology
is validated with the application for coherency identification (CI) in a
multi-machine power system (MMPS), an essential phenomenon in transient
stability analysis. The significance of PE condition and GCT is demonstrated
through various case studies implemented on 22 bus six generator system.
