Analyzing the impacts of data quality and availability on system stability analysis using single machine equivalents

In this paper we propose to analyze the effects which errant or missing data may have on the stability analysis of a system during transient events, applying single machine equivalent (SIME) modelling for the analyses. As modern power systems are updated with the latest technologies, the deployment of PMUs is expected to increase the observability of the network both in real-time and for post-event analysis. However, there is a risk that while the availability of this new data will lead to better observability and controllability, there is an increasing risk of relying on the availability of this data for ensuring system security. Furthermore, advancements in the power system are expected to increase the number of active consumers, where the deployment of small-scale distributed non-synchronous generation or sources of unmodelled inertia will impact the swing of the system during transients, but may not be possible to account for in system models. While SIME models have been shown to be useful in many situations, in this paper we seek to quantify the errors introduced into the stability analysis due to the mentioned data quality issues. This analysis allows an assessment of the performance of SIME in situations where data quality is nonoptimal, allowing for a determination of the viability of SIME in more realistic scenarios.