Voltage Instability Prediction Using a Deep Recurrent Neural Network
Journal article, 2021

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, where combinations 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 95 % 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.

Dynamic security assessment

voltage instability prediction

voltage stability assessment

long short-term memory

recurrent neural network


Hannes Hagmar

Chalmers, Electrical Engineering, Electric Power Engineering

Lang Tong

Cornell University

Robert Eriksson

Swedish national grid

Anh Tuan Le

Chalmers, Electrical Engineering, Electric Power Engineering

IEEE Transactions on Power Systems

0885-8950 (ISSN)

Vol. 36 1 17-27 9139280

Advanced visualization of voltage stability limit and system protection based on real-time measurement

Swedish Energy Agency (44358-1), 2016-06-01 -- 2020-12-31.

Swedish national grid, 2016-06-01 -- 2020-12-31.

Driving Forces

Sustainable development

Areas of Advance


Subject Categories

Energy Systems

Control Engineering

Other Electrical Engineering, Electronic Engineering, Information Engineering



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