Deep-learning Based Modeling of Fault Detachment Stability for Power Grid

TL;DR

This paper uses deep learning to model power grid fault stability, aiming to improve fault removal delay management by analyzing protection system failures and their impact on system stability.

Contribution

It introduces a deep learning approach to predict fault stability in power grids, providing insights into backup protection failure probabilities and system stability.

Findings

N-1 backup protection failure probability is about 2.5%.

Deep learning effectively models fault stability.

Protection system failures do not necessarily lead to instability.

Abstract

The project intends to model the stability of power system with a deep learning algorithm to the problem, aiming to delay the removal of the fault. The so-called "fail-delay cut-off" refers to the occurrence of N-1 backup protection action on the backbone network of the system, resulting in longer time for the removal of the fault. In practice, through the analysis and calculation of a large number of online data, we have found that the N-1 failure system of the main protection action will not be unstable, which is also a guarantee of the operation mode arrangement. In the case of the N-1 backup protection action, there is an approximately 2.5% probability that the system will be destabilized. Therefore, research is needed to improve the operating arrangement.