# Voltage Instability Prediction Using a Deep Recurrent Neural Network

## TL;DR

This paper introduces a deep recurrent neural network with long short-term memory for predicting voltage instability, providing accurate early warnings by analyzing real-time and historical data in power systems.

## Contribution

It presents a novel long sequence-based RNN approach for voltage instability prediction, outperforming shorter sequence models and traditional neural networks.

## Key findings

- Almost all N-1 contingency cases predicted correctly
- Over 93% accuracy in N-1-1 contingency cases seconds after disturbances
- Long sequence-based method significantly improves classification accuracy

## Abstract

This paper develops a new method for voltage instability prediction using a recurrent neural network with long short-term memory. The method is aimed to be used as a supplementary warning system for system operators, capable of assessing whether the current state will cause voltage instability issues several minutes into the future. The proposed method use a long sequence-based network, where both real-time and historic data are used to enhance the classification accuracy. The network is trained and tested on the Nordic32 test system, wherecombinations of different operating conditions and contingency scenarios are generated using time-domain simulations. The method shows that almost all N-1 contingency test cases were predicted correctly, and N-1-1 contingency test cases were predicted with over 93 % accuracy only seconds after a disturbance. Further, the impact of sequence length is examined, showing that the proposed long sequenced-based method provides significantly better classification accuracy than both a feedforward neural network and a network using a shorter sequence.