A Dataset Generation Toolbox for Dynamic Security Assessment: On the Role of the Security Boundary

TL;DR

This paper introduces a new dataset generation method focused on sampling near security boundaries in power systems, enhancing data-driven dynamic security assessment by capturing critical operating points for improved reliability.

Contribution

A novel systematic sampling method for generating datasets near security boundaries, considering AC feasibility and stability, to improve data-driven security assessment.

Findings

Sampling near security boundaries improves model accuracy.

Including boundary-adjacent points enhances security boundary detection.

Balanced datasets with secure and insecure points are crucial for training.

Abstract

Dynamic security assessment (DSA) is crucial for ensuring the reliable operation of power systems. However, conventional DSA approaches are becoming intractable for future power systems, driving interest in more computationally efficient data-driven methods. Efficient dataset generation is a cornerstone of these methods. While importance and generic sampling techniques often focus on operating points near the system's security boundary, systematic methods for sampling in this region remain scarce. Furthermore, the impact of sampling near the security boundary on the performance of data-driven DSA methods has yet to be established. This paper highlights the critical role of accurately capturing security boundaries for effective security assessment. As such, we propose a novel method for generating a high number of samples close to the security boundary, considering both AC feasibility and small-signal stability. Case studies on the PGLib-OPF 39-bus and PGLib-OPF 162-bus systems demonstrate the importance of including boundary-adjacent operating points in training datasets while maintaining a balanced distribution of secure and insecure points.