A Comprehensive Survey on Real-Time Voltage Stability Assessment for Power Systems

TL;DR

This paper reviews various techniques for real-time voltage stability assessment in power systems, emphasizing phasor measurement units, Thevenin equivalents, and AI methods, and compares traditional and modern approaches for stability evaluation.

Contribution

It provides a comprehensive comparison of traditional, Thevenin-based, and AI-driven methods for real-time voltage stability assessment in power systems.

Findings

Phasor measurement units enable real-time voltage stability monitoring.

Artificial Neural Networks offer fast, online stability margin estimation.

Thevenin equivalent methods are effective for stability analysis.

Abstract

Accurate real-time assessment of power systems voltage stability has been an active area of research in the past few decades. In the past decade, after the development of phasor measurement units (PMU), a lot of discussions has been going on phasor measurement techniques for real-time voltage stability. The fundamental idea behind these methods is to find the Thevenin equivalents of the system, and then determine the voltage stability margin based on the equivalent circuits. Some approaches also include the use of Artificial Neural Networks (ANN), for online monitoring of voltage stability margins. These methods are really fast as compared to the other methods. It has been shown that if we can obtain the phase angles and voltage magnitude in real-time from the phasor measurement units (PMU), then the voltage stability margins can be obtained in real-time and we can initiate voltage stability control methods. We are going to discuss Thevenin's equivalent methods and Artificial Intelligence methods in detail in this paper. We will also introduce the traditional methods which were earlier used for power systems stability assessment such as Time Domain methods, Static Methods, and Sensitivity methods. We are going to finally compare these methods and try to give general guidance on choosing a power stability method.