Open Access
Issue
E3S Web Conf.
Volume 658, 2025
Third International Conference of Applied Industrial Engineering: Intelligent Models and Data Engineering (CIIA 2025)
Article Number 01004
Number of page(s) 10
Section Industrial Optimization
DOI https://doi.org/10.1051/e3sconf/202565801004
Published online 13 November 2025
  1. M. Lockwood, “Transforming the grid for a more environmentally and socially sustainable electricity system in Great Britain is a slow and uneven process”, Proc. Natl. Acad. Sci. U. S. A., vol. 120, núm. 47, 2023, doi: 10.1073/pnas.2207825120. [Google Scholar]
  2. Q. Xiong et al., “Impact of Access to Electricity and Socio-Economic Environment on Poverty Reduction: An Empirical Study on Myanmar”, Energies, vol. 17, núm. 21, pp. 1–20, 2024, doi: 10.3390/en17215451. [Google Scholar]
  3. I. Murugesan y K. Sathish, “Gradient Ascent Optimization for Fault Detection in Electrical Power Systems based on Wavelet Tranformation”, Curr. Signal Transduct. Ther., vol. 15, núm. 3, pp. 294–302, ene. 2021, doi: 10.2174/1574362414666190619092910. [Google Scholar]
  4. D. Dwivedi et al., “Advancements in Enhancing Resilience of Electrical Distribution System :A Review on Frameworks, Metrics, and Technological Innovations”, nov. 2023, [En ínea]. Disponible en: http://arxiv.org/abs/2311.07050 [Google Scholar]
  5. N. G. O., “Mitigating Fault and Revenue Losses Uing Fault Detectors at Trans Amadi Industrial Layout, Port Harcourt Rivers State”, Int. J. Res. Appl. Sci. Eng. Technol., vol. 9, núm. 12, pp. 883–890, dic. 2021, doi: 10.22214/ijraset.2021.39072. [Google Scholar]
  6. R. G. Mohamed, M. A. Ebrahim, y S. H. E. Abdel Aleem, “Enhancing fault-clearing algorithm for renewable-energy-based distribution system using artificial neural networks”, Clean Energy, vol. 8, núm. 5, pp. 97–116, oct. 2024, doi: 10.1093/ce/zkae056. [Google Scholar]
  7. O. Kingsley Obi., C. C. Nwobu., A. C. Odigbo., y D. C. Oyiogu, “Artificial Neural Network Applications in Transmission Line Fault Diagnosis”, Int. J. Res. Innov. Appl. Sci., vol. IX, núm. VIII, pp. 48–62, 2024, doi: 10.51584/IJRIAS.2024.908006. [Google Scholar]
  8. H. W. Olalekan y O. I. Augustine, “Artificial Neural Network-Based Fault Detection on Nigerian 330kv Power Transmission Line”, Int. J. Res. Rev., vol. 11, núm. October, pp. 518–535, 2024. [Google Scholar]
  9. J. A. R. R. Jayasinghe, J. H. E. Malindi, R. M. A. M. Rajapaksha, V. Logeeshan, y C. Wanigasekara, “Classification and Localization of Fault in AC Microgrids Through Discrete wavelet Tranform and Artificial Neural Networks”, IEEE Open Access J. Power Energy, vol. 11, núm. May, pp. 303–313, 2024, doi: 10.1109/OAJPE.2024.3422387. [Google Scholar]
  10. H. A. Naji, R. A. Fayadh, y A. H. Mutlag, “Artificial intelligence-based fault classification for distribution line power cable”, 2024, p. 050034. doi: 10.1063/5.0236143. [Google Scholar]
  11. Y. Cao, J. Tang, S. Shi, D. Cai, L. Zhang, y P. Xiong, “Fault Diagnosis Techniques for Electrical Distribution Network Based on Artificial Intelligence and Signal Processing: A Review”, Processes, vol. 13, núm. 1, p. 48, dic. 2024, doi: 10.3390/pr13010048. [Google Scholar]
  12. H. Rezapour, S. Jamali, y A. Bahmanyar, “Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks”, Energies, vol. 16, núm. 12, 2023, doi: 10.3390/en16124636. [Google Scholar]
  13. J. Duan, “Deep larning anomaly detection in AI-powered intelligent power distribution systems”, Front. Energy Res., vol. 12, mar. 2024, doi: 10.3389/fenrg.2024.1364456. [Google Scholar]
  14. R. Islam, M. A. H. Rivin, S. Sultana, M. A. B. Asif, M. Mohammad, y M. Rahaman, “Machine learning for power system stability and control”, Results Eng., vol. 26, núm. April, p. 105355, 2025, doi: 10.1016/j.rineng.2025.105355. [Google Scholar]
  15. A. I. Rodríguez P. y X. D. Buitrago R., “How to choose an activation function for deep learning”, Tekhnê, vol. 19, núm. 1 SE-Artículos, pp. 23–32, 2022, [En línea]. Disponible en: https://geox.udistrital.edu.co/index.php/tekhne/article/view/20337 [Google Scholar]
  16. J. Zhang y Y. Xu, “Training feedforward Neural Networks Using an Enhanced Marine Predators Algorithm”, Processes, vol. 11, núm. 3, p. 924, mar. 2023, doi: 10.3390/pr11030924. [Google Scholar]
  17. M. Khodayar y J. Regan, “Deep Neural Networks in Power System : A Review”, Energies, vol. 16, núm. 12, p. 4773, jun. 2023, doi: 10.3390/en16124773. [Google Scholar]
  18. A. Y. Seidu, E. Twumasi, y E. A. Frimpong, “Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission lines”, AIMS Electron. Electr. Eng., vol. 8, núm. 4, pp. 508–541, 2024, doi: 10.3934/electreng.2024024. [Google Scholar]
  19. V. N. Ogar, S. Hussain, y K. A. A. Gamage, “The use of artificial neural network for low latency of fault detection and localisation in transmission line”, Heliyon, vol. 9, núm. 2, p. e13376, feb. 2023, doi: 10.1016/j.heliyon2023.e13376. [Google Scholar]
  20. G. K. Yadav, M. K. Kirar, S. C. Gupta, y J. Rajender, “Integrating ANN and ANFIS for effective fault detection and location in modern power grid”, Sci. Technol. Energy Transit., vol. 80, 2025, doi: 10.2516/stet/2025013. [Google Scholar]
  21. M. M. Morovati, A. Nikanjam, y F. Khomh, “Fault Localization in Deep Learning-based Software: A System-level Approach”, ACM Trans. Softw. Eng. Methodol., vol. 1, núm. 1, pp. 1–30, 2024, [En línea]. Disponible en: http://arxiv.org/abs/2411.08172 [Google Scholar]
  22. X. Zhang, C. Liu, y Z. Sun, “Line Fault Detection of DC Distribution Ntworks Using the Artificial Neural Network”, Energy Eng., vol. 120, núm. 7, pp. 1667–1683, 2023, doi: 10.32604/ee.2023.025186. [Google Scholar]
  23. M. A. Khan, B. Asad, T. Vaimann, A. Kallaste, R. Pomarnacki, y V. K. Hyunh, “Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data an Machin Learning Algorithms”, Machines, vol. 11, núm. 10, p. 963, oct. 2023, doi: 10.3390/machines11100963. [Google Scholar]
  24. K. Moloi, N. W. Ndlela, y I. E. Davidson, “Fault Classification and Localization Scheme for Power Distribution Network”, Appl. Sci., vol. 12, núm. 23, p. 11903, nov. 2022, doi: 10.3390/app122311903. [Google Scholar]
  25. K. Rajanikanth y N. Sharma, “Artificial Neural Network Based Reconfiguration Of Electrical Distribution Network”, Int. J. Creat. Res. Thoughts, vol. 10, núm. 1, pp. 798–805, 2022. [Google Scholar]
  26. A. S. Alhanaf, H. H. Balik, y M. Farsadi, “Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks”, Energies, vol. 16, núm. 22, p. 7680, nov. 2023, doi: 10.3390/en16227680. [Google Scholar]
  27. M. A. Bin Syed, M. R. Hasan, N. I. Chowdhury, M. H. Rahman, y I. Ahmed, “A systematic review of tim series algorithms and analytics in predictive maintenance”, Decis. Anal. J., vol. 15, núm. April, p. 100573, 2025, doi: 10.1016/j.dajour.2025.100573. [Google Scholar]
  28. M. Z. Yousaf, A. R. Singh, S. Khalid, M. Bajaj, B. H. Kumar, y I. Zaitsev, “Bayesian-optimized LSTM-DWT approach for reliable fault detection in MMC-based HVDC systems”, Sci. Rep., vol. 14, núm. 1, pp. 1–24, 2024, doi: 10.1038/s41598-024-68985-5. [CrossRef] [Google Scholar]
  29. Y. Zhang, X. Wang, Y. Luo, Y. Xu, J. He, y G. Wu, “A CNN based transfer learning method for high impedance fault detection”, IEEE Power Energy Soc. Gen. Meet., vol. 2020-Augus, 2020, doi: 10.1109/PESGM41954.2020.9281671. [Google Scholar]
  30. I. Mitiche, A. Nesbitt, S. Conner, P. Boreham, y G. Morison, “1D-CNN based real-time fault detection system for power asset diagnostics”, IET Gener. Transm. Distrib., vol. 14, núm. 24, pp. 5766–5773, dic. 2020, doi: 10.1049/iet-gtd.2020.0773. [Google Scholar]
  31. H. Radmanesh y A. Hadadi, “A hybrid machine learning and ied-based fault detection scheme for microgrids”, Results Eng., vol. 26, núm. May, p. 105369, 2025, doi: 10.1016/j.rineng.2025.105369. [Google Scholar]
  32. H. Shah, N. Chothani, y J. Chakravorty, “Fault Detection and Classification in Interconnected System with Wind Generation Uing ANN and SVM”, Adv. Electr. Electron. Eng., vol. 20, núm. 3, pp. 225–239, 2022, doi: 10.15598/aeee.v20i3.4483. [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.