Deep Learning for Short-Term Voltage Stability Assessment of Power Systems

TL;DR

This paper introduces a deep learning approach using LSTM networks for real-time short-term voltage stability assessment in power systems, effectively capturing dynamic temporal dependencies from post-disturbance trajectories.

Contribution

It develops a semi-supervised clustering method to label data and constructs an LSTM-based model that outperforms traditional shallow learning methods in stability assessment.

Findings

Accurate and timely stability assessment on IEEE 39-bus system

Superiority over traditional shallow learning methods

Effective learning of temporal dependencies from dynamic trajectories

Abstract

To fully learn the latent temporal dependencies from post-disturbance system dynamic trajectories, deep learning is utilized for short-term voltage stability (STVS) assessment of power systems in this paper. First of all, a semi-supervised cluster algorithm is performed to obtain class labels of STVS instances due to the unavailability of reliable quantitative criteria. Secondly, a long short-term memory (LSTM) based assessment model is built through learning the time dependencies from the post-disturbance system dynamics. Finally, the trained assessment model is employed to determine the systems stability status in real time. The test results on the IEEE 39-bus system suggest that the proposed approach manages to assess the stability status of the system accurately and timely. Furthermore, the superiority of the proposed method over traditional shallow learning-based assessment methods has also been proved.