Efficient Sampling for Data-Driven Frequency Stability Constraint via Forward-Mode Automatic Differentiation

TL;DR

This paper introduces a gradient-based sampling method using forward-mode automatic differentiation to efficiently generate balanced stable and unstable samples for data-driven frequency stability constraints in power systems.

Contribution

It proposes a novel sampling algorithm that improves data quality for stability models by leveraging sensitivity states and gradient surgery, outperforming existing methods.

Findings

The method produces more balanced training datasets.

It demonstrates superior performance over unrolling differentiation.

The approach enhances the accuracy of frequency stability models.

Abstract

Encoding frequency stability constraints in the operation problem is challenging due to its complex dynamics. Recently, data-driven approaches have been proposed to learn the stability criteria offline with the trained model embedded as a constraint of online optimization. However, random sampling of stationary operation points is less efficient in generating balanced stable and unstable samples. Meanwhile, the performance of such a model is strongly dependent on the quality of the training dataset. Observing this research gap, we propose a gradient-based data generation method via forward-mode automatic differentiation. In this method, the original dynamic system is augmented with new states that represent the dynamic of sensitivities of the original states, which can be solved by invoking any ODE solver for a single time. To compensate for the contradiction between the gradient of various frequency stability criteria, gradient surgery is proposed by projecting the gradient on the normal plane of the other. In the end, we demonstrate the superior performance of the proposed sampling algorithm, compared with the unrolling differentiation and finite difference. All codes are available at https://github.com/xuwkk/frequency_sample_ad.