Real-Time Short-Term Voltage Stability Assessment Using Combined Temporal Convolutional Neural Network and Long Short-Term Memory Neural Network
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 (stab...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/13/6333 |
| _version_ | 1797481082568835072 |
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| author | Ananta Adhikari Sumate Naetiladdanon Anawach Sangswang |
| author_facet | Ananta Adhikari Sumate Naetiladdanon Anawach Sangswang |
| author_sort | Ananta Adhikari |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T22:09:24Z |
| format | Article |
| id | doaj.art-6f2d03965e8944bd922ef218ddc7303f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:09:24Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6f2d03965e8944bd922ef218ddc7303f2023-11-23T19:34:51ZengMDPI AGApplied Sciences2076-34172022-06-011213633310.3390/app12136333Real-Time Short-Term Voltage Stability Assessment Using Combined Temporal Convolutional Neural Network and Long Short-Term Memory Neural NetworkAnanta Adhikari0Sumate Naetiladdanon1Anawach Sangswang2Department of Electrical Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, ThailandDepartment of Electrical Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, ThailandDepartment of Electrical Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, ThailandThis 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.https://www.mdpi.com/2076-3417/12/13/6333fault-induced delayed voltage recoverylong short-term memoryobservation time windowshort-term voltage stabilitytemporal convolutional neural networktransfer learning |
| spellingShingle | Ananta Adhikari Sumate Naetiladdanon Anawach Sangswang Real-Time Short-Term Voltage Stability Assessment Using Combined Temporal Convolutional Neural Network and Long Short-Term Memory Neural Network Applied Sciences fault-induced delayed voltage recovery long short-term memory observation time window short-term voltage stability temporal convolutional neural network transfer learning |
| title | Real-Time Short-Term Voltage Stability Assessment Using Combined Temporal Convolutional Neural Network and Long Short-Term Memory Neural Network |
| title_full | Real-Time Short-Term Voltage Stability Assessment Using Combined Temporal Convolutional Neural Network and Long Short-Term Memory Neural Network |
| title_fullStr | Real-Time Short-Term Voltage Stability Assessment Using Combined Temporal Convolutional Neural Network and Long Short-Term Memory Neural Network |
| title_full_unstemmed | Real-Time Short-Term Voltage Stability Assessment Using Combined Temporal Convolutional Neural Network and Long Short-Term Memory Neural Network |
| title_short | Real-Time Short-Term Voltage Stability Assessment Using Combined Temporal Convolutional Neural Network and Long Short-Term Memory Neural Network |
| title_sort | real time short term voltage stability assessment using combined temporal convolutional neural network and long short term memory neural network |
| topic | fault-induced delayed voltage recovery long short-term memory observation time window short-term voltage stability temporal convolutional neural network transfer learning |
| url | https://www.mdpi.com/2076-3417/12/13/6333 |
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