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

TL;DR

The paper tackles the need for large, representative datasets to train data-driven stability models in power systems with high inverter-based resource penetration. It introduces an open-source HPC framework that defines a scalable operating space, uses adaptive, sensitivity-guided sampling, and performs small-signal stability analyses to populate high-information datasets. A case study on the NREL 118-bus system demonstrates that sensitivity-driven sampling concentrates samples near the stability margin and yields higher ML model accuracy (~92%) than non-sensitivity approaches. The framework is released as open-source and validated with admittance-based dynamic equivalents and parallel HPC execution, underscoring its practical impact for reliability assessment in modern grids.

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.

Figures (13)

• Figure 1: Data generation tool workflow.
• Figure 2: Dimensions and variables of the operating space.
• Figure 3: Definition of the exploration process.
• Figure 4: Scheme of the NREL 118-bus system.
• Figure 5: Control schemes

Theorems & Definitions (4)

• Definition 1
• Definition 2
• Definition 3
• Definition 4