Open Access
| Issue |
E3S Web Conf.
Volume 680, 2025
The 4th International Conference on Energy and Green Computing (ICEGC’2025)
|
|
|---|---|---|
| Article Number | 00054 | |
| Number of page(s) | 11 | |
| DOI | https://doi.org/10.1051/e3sconf/202568000054 | |
| Published online | 19 December 2025 | |
- N. Bouchiba and A. Kaddouri, “Fault detection and localization based on Decision Tree and Support vector machine algorithms in electrical power transmission network,” in 2022 2nd International Conference on Advanced Electrical Engineering (ICAEE), 2022: IEEE, pp. 1-6. [Google Scholar]
- R. Alilouch and F. Slaoui-Hasnaoui, “Intelligent Relay Based on Artificial Neural Networks ANN for Transmission Line,” in 2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), 2022: IEEE, pp. 468-473. [Google Scholar]
- J. Gracia, A. Mazon, and I. Zamora, “Best ANN structures for fault location in single-and double-circuit transmission lines,” IEEE transactions on power delivery, vol. 20, no. 4, pp. 2389-2395, 2005. [Google Scholar]
- A. Ahmed et al., “Cyber physical security analytics for anomalies in transmission protection systems,” IEEE Transactions on Industry Applications, vol. 55, no. 6, pp. 6313-6323, 2019. [Google Scholar]
- M. Bhatnagar, A. Yadav, A. Swetapadma, and A. Y. Abdelaziz, “LSTM-based low-impedance fault and high-impedance fault detection and classification,” Electrical Engineering, vol. 106, no. 5, pp. 6589-6613, 2024. [Google Scholar]
- S. Belagoune, N. Bali, A. Bakdi, B. Baadji, and K. Atif, “Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems,” Measurement, vol. 177, p. 109330, 2021. [Google Scholar]
- P. Park, P. D. Marco, H. Shin, and J. Bang, “Fault detection and diagnosis using combined autoencoder and long short-term memory network,” Sensors, vol. 19, no. 21, p. 4612, 2019. [Google Scholar]
- J. Azar, Y. Laarouchi, F. Bouzon, and R. Couturier, “Détection d’anomalies pour les réseaux smart-grids basée sur un autoencodeur LSTM,” in Conference on Artificial Intelligence for Defense, 2022. [Google Scholar]
- A. Ahmed, K. S. Sajan, A. Srivastava, and Y. Wu, “Anomaly detection, localization and classification using drifting synchrophasor data streams,” IEEE Transactions on Smart Grid, vol. 12, no. 4, pp. 3570-3580, 2021. [Google Scholar]
- J. Zhou, Y. Fu, Y. Wu, H. Xia, Y. Fang, and H. Lu, “Anomaly detection over concept drifting data streams,” Journal of Computational Information Systems, vol. 5, no. 6, pp. 1697-1703, 2009. [Google Scholar]
- K. Yu, W. Shi, and N. Santoro, “Designing a streaming algorithm for outlier detection in data mining—An incremental approach,” Sensors, vol. 20, no. 5, p. 1261, 2020. [Google Scholar]
- Y. Xia, F. Yu, X. Xiong, Q. Huang, and Q. Zhou, “A novel microgrid islanding detection algorithm based on a multi-feature improved LSTM,” Energies, vol. 15, no. 8, p. 2810, 2022. [Google Scholar]
- A. Ahmed, “PMUNET: Anomaly Detection Over Concept Drifting Synchrophasor Data Streams,” Washington State University, 2019. [Google Scholar]
- N. Bouchiba, “Application de L’intelligence Artificielle Pour la Détection et la Localisation des Défauts Dans les Réseaux Électriques,” Universite de Moncton (Canada), 2024. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

