Real-Time Short-Term Voltage Stability Assessment Using Combined Temporal Convolutional Neural Network and Long Short-Term Memory Neural Network
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Publication Details
Author list: dhikari, Ananta; Naetiladdanon, Sumate; Sangswang, Anawach;
Publisher: MDPI
Publication year: 2022
Volume number: 12
Issue number: 13
ISSN: 2076-3417
eISSN: 2076-3417
Languages: English-Great Britain (EN-GB)
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Abstract
This research presents a new method based on a combined temporal convolutional neural network and long-short term memory neural network for the real-time assessment of short-term voltage stability to keep the electric grid in a secure state. The assessment includes both the voltage instability (stable state or unstable state) and the fault-induced delayed voltage recovery phenomenon subjected to disturbance. The trained model uses the time series post-disturbance bus voltage trajectories as the input in order to predict the stability state of the power system in a computationally efficient manner. The proposed method also utilizes a transfer learning approach that acclimates to the pre-trained model using only a few labeled samples, which assesses voltage instability under unseen network topology change conditions. Finally, the performance evaluated on the IEEE 9 Bus and New England 39 Bus test systems shows that the proposed method gives superior accuracy with higher efficacy and thus is suitable for online application. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords
fault-induced delayed voltage recovery, observation time window, short-term voltage stability, temporal convolutional neural network
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