PMU Data Feature Considerations for Realistic, Synthetic Data Generation

TL;DR

This paper analyzes key features of PMU data, such as variability, anomalies, and missing data, to improve the realism of synthetic measurements for power system analysis.

Contribution

It identifies essential data features and proposes their inclusion to enhance the realism of synthetic PMU data for grid studies.

Findings

Including data anomalies improves realism.

Variability due to system dynamics is critical.

Missing data samples mimic real-world conditions.

Abstract

It is critical that the qualities and features of synthetically-generated, PMU measurements used for grid analysis matches those of measurements obtained from field-based PMUs. This ensures that analysis results generated by researchers during grid studies replicate those outcomes typically expected by engineers in real-life situations. In this paper, essential features associated with industry PMU-derived data measurements are analyzed for input considerations in the generation of vast amounts of synthetic power system data. Inherent variabilities in PMU data as a result of the random dynamics in power system operations, oscillatory contents, and the prevalence of bad data are presented. Statistical results show that in the generation of large datasets of synthetic, grid measurements, an inclusion of different data anomalies, ambient oscillation contents, and random cases of missing data samples due to packet drops helps to improve the realism of experimental data used in power systems analysis.