# Steady-state data-driven dynamic stability assessment in the Korean power system

## TL;DR

This paper proposes a method to assess power system stability using steady-state data, avoiding the need for expensive high-resolution measurements.

## Contribution

A novel framework for rotor angle stability prediction using steady-state data and SVM with reduced dimensionality.

## Key findings

- The framework effectively identifies unstable cases using 5-min-interval power flow data.
- Feature extraction and SVM training improved prediction accuracy for critical line faults.
- Partitioning data by month accounts for system topology changes, enhancing real-time response.

## Abstract

The extensive research on dynamic security assessment stability prediction has focused on data preprocessing techniques to improve accuracy because it was assumed that high-resolution postfault data exist. For practical users, the acquisition and application of high-resolution measurement data present significant challenges. Installing phasor measurement units on all power system nodes is deemed impractical due to high costs. In this work, we aimed to develop a rotor angle stability prediction model using steady-state data that can be easily generated from the current energy management system. Note that the steady-state measurement data refer to a pre-contingency operation condition characterized by real and reactive loads, generation levels, flows, as well as voltages and angles. The proposed framework comprises three stages: it finds physical meaning from the extended equal-area criterion to move away from the black-box approach, proposes a feature data extraction strategy to reduce the dimensionality of the input space in the support vector machine, and partition time-series power flow data by month to consider system topology changes. By utilizing 5-min-interval power flow data, unstable cases are determined, and two main feature data are extracted to train the support vector machine. The obtained results showed the effectiveness of the proposed framework in responding to a critical line fault event in real time.