Voltage Instability Prediction Using a Deep Recurrent Neural Network
Artikel i vetenskaplig tidskrift, 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, Elektroteknik, Elkraftteknik

Lang Tong

Cornell University

Robert Eriksson

Svenska kraftnät

Anh Tuan Le

Chalmers, Elektroteknik, Elkraftteknik

IEEE Transactions on Power Systems

0885-8950 (ISSN) 15580679 (eISSN)

Vol. 36 1 17-27 9139280

Avancerad visualisering av spänningsstabilitetsgränser och systemskydd baserat på realtidsmätningar

Energimyndigheten (44358-1), 2016-06-01 -- 2020-12-31.

Svenska kraftnät, 2016-06-01 -- 2020-12-31.


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