Efficient Creation of Datasets for Data-Driven Power System Applications

TL;DR

This paper introduces a fast, scalable method for generating balanced datasets of secure and insecure power system operating points, facilitating data-driven applications in power system security analysis.

Contribution

It presents an infeasibility certificate based on separating hyperplanes that efficiently characterizes large insecure regions, enabling creation of balanced datasets with reduced computation.

Findings

Significant reduction in computation time for dataset creation.

Ability to handle larger control variable sets (up to 125 variables).

Creation of balanced datasets of secure and insecure points for power systems.

Abstract

Advances in data-driven methods have sparked renewed interest for applications in power systems. Creating datasets for successful application of these methods has proven to be very challenging, especially when considering power system security. This paper proposes a computationally efficient method to create datasets of secure and insecure operating points. We propose an infeasibility certificate based on separating hyperplanes that can a-priori characterize large parts of the input space as insecure, thus significantly reducing both computation time and problem size. Our method can handle an order of magnitude more control variables and creates balanced datasets of secure and insecure operating points, which is essential for data-driven applications. While we focus on N-1 security and uncertainty, our method can extend to dynamic security. For PGLib-OPF networks up to 500 buses and up to 125 control variables, we demonstrate drastic reductions in unclassified input space volumes and computation time, create balanced datasets, and evaluate an illustrative data-driven application.