  Large-scale blackouts that have occurred in the past few decades have
necessitated the need to do extensive research in the field of grid security
assessment. With the aid of synchrophasor technology, which uses phasor
measurement unit (PMU) data, dynamic security assessment (DSA) can be performed
online. However, existing applications of DSA are challenged by variability in
system conditions and unaccounted for measurement errors. To overcome these
challenges, this research develops a DSA scheme to provide security prediction
in real-time for load profiles of different seasons in presence of realistic
errors in the PMU measurements. The major contributions of this paper are: (1)
develop a DSA scheme based on PMU data, (2) consider seasonal load profiles,
(3) account for varying penetrations of renewable generation, and (4) compare
the accuracy of different machine learning (ML) algorithms for DSA with and
without erroneous measurements. The performance of this approach is tested on
the IEEE-118 bus system. Comparative analysis of the accuracies of the ML
algorithms under different operating scenarios highlights the importance of
considering realistic errors and variability in system conditions while
creating a DSA scheme.
