  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.
