| Title |
LSTM-based fault classification model in transmission lines for real fault data |
| Authors |
김태근(Taegeun Kim) ; 임세헌(Seheon Lim) ; 송경민(Kyungmin Song) ; 윤성국(Sung-Guk Yoon) |
| DOI |
https://doi.org/10.5370/KIEE.2024.73.3.585 |
| Keywords |
Transmission line fault; Fault classification; Deep learning; fault simulation |
| Abstract |
Most studies on the line fault classification in transmission lines are conducted based on the simulation data because of the lack of fault data in the real world. However, the real fault signals differ from simulation data due to the presence of noise and uncertainty of fault parameters. As a result, the performance of previous fault classification studies based on the simulation data may not work accurately when they are used in real fault data. In this research, we compared the classification performance of some previous fault classification works based on simulation data with real fault data. The result showed that the classification performance was severely reduced with real fault data. To overcome this mismatch between simulation and real fault data, we propose a fault classification model for real fault data. The proposed classification model combined the long short-term memory (LSTM) layer and fully connected (FC) layer with different input features to train time series and non-time series features from the data. The result shows that the proposed model classifies fault type with high accuracy for both simulation data and real fault data. |