Mitigating the Risk of Voltage Collapse using Statistical Measures from PMU Data

TL;DR

This paper introduces a statistical approach using PMU data to monitor and enhance voltage stability in power systems, effectively preventing voltage collapse through dynamic control actions.

Contribution

It presents a novel method that uses statistical measures from PMU data to identify critical voltage variances and implement a reactive power controller for improved stability.

Findings

Statistical measures can predict voltage collapse risk.

The proposed controller outperforms traditional feedback systems.

Effective in dynamic load conditions.

Abstract

With the continued deployment of synchronized Phasor Measurement Units (PMUs), high sample rate data are rapidly increasing the real time observability of power systems. Prior research has shown that the statistics of these data can provide useful information regarding network stability, but it is not yet known how this statistical information can be actionably used to improve power system stability. To address this issue, this paper presents a method that gauges and improves the voltage stability of a system using the statistics present in PMU data streams. Leveraging an analytical solver to determine a range of "critical" bus voltage variances, the presented methods monitor raw statistical data in an observable load pocket to determine when control actions are needed to mitigate the risk of voltage collapse. A simple reactive power controller is then implemented, which acts dynamically to maintain an acceptable voltage stability margin within the system. Time domain simulations on 3-bus and 39-bus test cases demonstrate that the resulting statistical controller can out-perform more conventional feedback control systems by maintaining voltage stability margins while loads simultaneously increase and fluctuate.