Data-Driven Transient Stability Boundary Generation for Online Security Monitoring

TL;DR

This paper introduces a data-driven method to efficiently generate transient stability boundaries for power system security monitoring, reducing computational load by focusing on critical scenarios near the stability boundary.

Contribution

It develops a novel search strategy and scenario selection mechanism to accurately and efficiently update stability boundaries in real-time power system monitoring.

Findings

Effective boundary generation with fewer samples

Validated on two case studies

Improves online security monitoring efficiency

Abstract

Transient stability boundary (TSB) is an important tool in power system online security monitoring, but practically it suffers from high computational burden using state-of-the-art methods, such as time-domain simulation (TDS), with numerous scenarios taken into account (e.g., operating points (OPs) and N-1 contingencies). The purpose of this work is to establish a data-driven framework to generate sufficient critical samples close to the boundary within a limited time, covering all critical scenarios in current OP. Therefore, accurate TSB can be periodically refreshed by tracking current OP in time. The idea is to develop a search strategy to obtain more data samples near the stability boundary, while traverse the rest part with fewer samples. To achieve this goal, a specially designed transient index sensitivity based search strategy and critical scenarios selection mechanism are proposed, in order to find out the most representative scenarios and periodically update TSB for online monitoring. Two case studies validate effectiveness of the proposed method.