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Real-Time Short-Term Voltage Stability Assessment Using Combined Temporal Convolutional Neural Network and Long Short-Term Memory Neural Network
| Content Provider | MDPI |
|---|---|
| Author | Adhikari, Ananta Naetiladdanon, Sumate Sangswang, Anawach |
| Copyright Year | 2022 |
| Description | 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. |
| Starting Page | 6333 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app12136333 |
| Journal | Applied Sciences |
| Issue Number | 13 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-06-22 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Industrial Engineering Fault-induced Delayed Voltage Recovery Long Short-term Memory Observation Time Window Short-term Voltage Stability Temporal Convolutional Neural Network Transfer Learning |
| Content Type | Text |
| Resource Type | Article |
