ARTIFICIAL NEURAL NETWORK MODELS OF CROSS-LINKED POLYETHYLENE
Abstract
Keywords
Power cables , Insulation models , Cross-linked polyethylene , ANN model , Parameter prediction
References
- Arikan, O., Uydur, C. C., & Kumru, C. F. (2022). Prediction of dielectric parameters of an aged MV cable: A comparison of curve fitting, decision tree and artificial neural network methods. Electric Power Systems Research, 208, 107892. https://doi.org/10.1016/j.epsr.2022.107892
- Ashok, N., Soman, K. P., Samanta, M., Sruthi, M. S., Poornachandran, P., Devi V. G, S., & Sukumar, N. (2024). Polymer and Nanocomposite Informatics: Recent Applications of Artificial Intelligence and Data Repositories. Advanced Machine Learning with Evolutionary and Metaheuristic Techniques, 297-322. https://doi.org/10.1007/978-981-99-9718-3_12
- Boukezzi, L., & Boubakeur, A. (2013). Prediction of mechanical properties of XLPE cable insulation under thermal aging: neural network approach. IEEE Transactions on Dielectrics and Electrical Insulation, 20(6), 2125-2134. https://doi.org/10.1109/TDEI.2013.6678861
- Cole, K. S., & Cole, R. H. (1941). Dispersion and absorption in dielectrics I. Alternating current characteristics. The Journal of chemical physics, 9(4), 341-351. https://doi.org/10.1063/1.1750906
- Çanta, H., Mutlu, R., & Korkmaz Tan, R. (2024). Yeni Üretilen XLPE İzolasyonlu Tek Damarlı Bir Güç Kablosunun Kaçak Empedansının Hesabı. EMO Bilimsel Dergi, 14(1), 19-26.