Data Generation for Stability Studies of Power Systems with High Penetration of Inverter-Based Resources

TL;DR

This paper introduces an open-source Python framework for systematically generating high-quality datasets to assess stability in power systems with high inverter-based resources, aiding data-driven analysis.

Contribution

It presents a scalable, adaptive sampling framework that efficiently explores power system stability margins for modern high-IBR penetration scenarios.

Findings

Framework efficiently targets stability margin regions.

Enables comprehensive datasets for machine learning models.

Supports stability assessment in evolving power systems.

Abstract

The increasing penetration of inverter-based resources (IBRs) is fundamentally reshaping power system dynamics and creating new challenges for stability assessment. Data-driven approaches, and in particular machine learning models, require large and representative datasets that capture how system stability varies across a wide range of operating conditions and control settings. This paper presents an open-source, high-performance computing framework for the systematic generation of such datasets. The proposed tool defines a scalable operating space for large-scale power systems, explores it through an adaptive sampling strategy guided by sensitivity analysis, and performs small-signal stability assessments to populate a high-information-content dataset. The framework efficiently targets regions near the stability margin while maintaining broad coverage of feasible operating conditions. The workflow is fully implemented in Python and designed for parallel execution. The resulting tool enables the creation of high-quality datasets that support data-driven stability studies in modern power systems with high IBR penetration.