Open Access
| Issue |
E3S Web Conf.
Volume 664, 2025
4th International Seminar of Science and Applied Technology: “Green Technology and AI-Driven Innovations in Sustainability Development and Environmental Conservation” (ISSAT 2025)
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|---|---|---|
| Article Number | 01005 | |
| Number of page(s) | 11 | |
| Section | Artificial Intelligence and Human-Computer Interaction | |
| DOI | https://doi.org/10.1051/e3sconf/202566401005 | |
| Published online | 20 November 2025 | |
- H. Liu, L. Jiao, H. Lin, and L. Li, Design and Application Research of Portable Reversible Thermochromic Patches in Grid Power Supply Systems. Aip Advances. 14, no. 4 (2024). doi: 10.1063/5.0189608 [Google Scholar]
- W. Wang, X. Yan, S. Li, L. Zhang, J. Ouyang, and X. Ni, Failure of Submarine Cables Used in High‐voltage Power Transmission: Characteristics, Mechanisms, Key Issues and Prospects. Iet Generation Transmission & Distribution. 15, no. 9, pp. 1387–1402 (2021). doi: 10.1049/gtd2.12117 [Google Scholar]
- A. S. Alghamdi and R. K. Desuqi, A Study of Expected Lifetime of XLPE Insulation Cables Working at Elevated Temperatures by Applying Accelerated Thermal Ageing. Heliyon. 6, no. 1, p. e03120 (2020). doi: 10.1016/j.heliyon.2019.e03120 [Google Scholar]
- Y. Li, Z. Peng, D. Xu, S. Huang, Y. Gao, and Y. Li, Research on the Thermal Aging Characteristics of Crosslinked Polyethylene Cables Based on Polarization and Depolarization Current Measurement. Energies. 17, no. 10, p. 2274 (2024). doi: 10.3390/en17102274 [Google Scholar]
- E. E. Sherkawy, L. Nasrat, and M. Rihan, The Effect of Thermal Ageing on Electrical and Mechanical Properties of Thermoplastic Nanocomposite Insulation of Power High- Voltage Cables. Electrical Engineering & Electromechanics. 3, pp. 66–71 (2024) doi: 10.20998/2074-272x.2024.3.09 [Google Scholar]
- B. C. Kok, M. Looi, and H. H. Goh, Insulated Gate Bipolar Transistor Failure Analysis in Overvoltage Condition. Renewable Energy and Power Quality Journal. 10, no. 4 (2024). doi: 10.24084/repqj10.392 [Google Scholar]
- P. Sun, Z. Li, W. Sima, T. Yuan, M. Yang, K. Fan, X. Chen, and W. Pang, Morning Glory-Inspired Dual-Function Microcapsules for the Self-Reporting and Self-Healing of Electrothermal-Induced Damage of Electrical Devices. Acs Applied Materials & Interfaces. (2024). doi: 10.1021/acsami.3c18483 [Google Scholar]
- D. Ma, Y. Liu, L. Zheng, J. Gao, Z. Gao, and Z. Zhang, Prediction of Thermally Induced Failure for Electronic Equipment Based on an Artificial Olfactory System. Measurement Science and Technology. 32, no. 3, p. 035103 (2020). doi: 10.1088/1361-6501/abc9fa [Google Scholar]
- E. Mustafa, R. S. A. Afia, and Z. Á. Tamus, Dielectric Loss and Extended Voltage Response Measurements for Low-Voltage Power Cables Used in Nuclear Power Plant: Potential Methods for Aging Detection Due to Thermal Stress. Electrical Engineering. 103, no. 2, pp. 899–908 (2020). doi: 10.1007/s00202-020-01121-4 [Google Scholar]
- L. Jin, Z. Zhou, Y. Li, Z. Zou, and W. Zhao, Hotspot Temperature Prediction of Relay Protection Equipment Based on a Physical-Model-Aided Data-Driven Method. Energies. 17, no. 4, p. 816 (2024). doi: 10.3390/en17040816 [Google Scholar]
- V. S. K. Reddy, T. Saravanan, N. T. Velusudha, and T. Selwyn, Smart Grid Management System Based on Machine Learning Algorithms for Efficient Energy Distribution. E3s Web of Conferences. 387, p. 02005 (2023). doi: 10.1051/e3sconf/202338702005 [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
- A. Dutt and G. Karuna, Machine Learning Approaches for Fault Detection in Renewable Microgrids. Matec Web of Conferences. 392, p. 01192 (2024). doi: 10.1051/matecconf/202439201192 [CrossRef] [EDP Sciences] [Google Scholar]
- A. Bouhafs, M. R. Kafi, M. L. Louazene, B. Rouabah, and H. Toubakh, Fault-Detection- Based Machine Learning Approach to Multicellular Converters Used in Photovoltaic Systems. Machines. 10, no. 11, p. 992 (2022). doi: 10.3390/machines10110992 [Google Scholar]
- T. T. Ağır, Using Machine Learning Algorithms for Classifying Transmission Line Faults. Dümf Mühendislik Dergisi. (2022). doi: 10.24012/dumf.1096691 [Google Scholar]
- S. Rodrigues, G. Mütter, H. G. Ramos, and F. Morgado‐Dias, Machine Learning Photovoltaic String Analyzer. Entropy. 22, no. 2, p. 205 (2020). doi: 10.3390/e22020205 [Google Scholar]
- K. Barrera, Á. Sapena-Bañó, J. Martínez‐Román, and R. Puche‐Panadero, Implementing Deep Learning Models in Embedded Systems for Diagnosis Induction Machine. International Journal of Electrical and Computer Engineering Research. 3, no. 1, pp. 7–12 (2023). doi: 10.53375/ijecer.2023.319 [Google Scholar]
- D. Gutiérrez-Rojas, I. T. Christou, D. T. Dantas, A. Narayanan, P. H. J. Nardelli, and Y. Yang, Performance Evaluation of Machine Learning for Fault Selection in Power Transmission Lines. Knowledge and Information Systems. 64, no. 3, pp. 859–883 (2022). doi: 10.1007/s10115-022-01657-w [Google Scholar]
- M. H. Hamdan and D. C. Roach, The Sigmoid Neural Network Activation Function and Its Connections to Airy’s and the Nield-Kuznetsov Functions. 2, pp. 108–114 (2022). doi: 10.37394/232020.2022.2.13 [Google Scholar]
- Y. V. Khodnevych and D. V. Stefanyshyn, Do We Need a More Sophisticated Multilayer Artificial Neural Network to Compute Roughness Coefficient?. Environmental Safety and Natural Resources. 48, no. 4, pp. 170–182 (2023). doi: 10.32347/2411- 4049.2023.4.170-182 [Google Scholar]
- M. H. Farrell, T. Liang, and S. Misra, Deep Neural Networks for Estimation and Inference. Econometrica. 89, no. 1, pp. 181–213 (2021). doi: 10.3982/ecta16901 [Google Scholar]
- R. Sharma and S. Hosder, Mission-Driven Inverse Design of Blended Wing Body Aircraft With Machine Learning. Aerospace. 11, no. 2, p. 137 (2024). doi: 10.3390/aerospace11020137 [Google Scholar]
- Á. E. M. Zavala, J. E. Macías‐Díaz, D. Alba-Cuéllar, and J. A. Guerrero-Díaz-de-León, A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation With Time Series. Algorithms. 17, no. 2, p. 76 (2024). doi: 10.3390/a17020076 [Google Scholar]
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