Publications
[1] M. Asprou, C. Panayiotou and A. Stavrou, “A Hybrid Approach for Enhancing Line Parameter Estimation in Power Systems,” 2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE), Dubrovnik, Croatia, Feb. 2025, doi:10.1109/ISGTEUROPE62998.2024.10863387.
[4] M. Asprou, T. Avraamides, C. Panagiotou, and A. Stavrou, “Zero Sequence Line Parameter Estimation Using PMU Data Considering Uncertainty Factors,” presented at IEEE PowerTech 2025: Powering the Energy System Transformation, Kiel, Germany, Oct. 2025. doi: 10.1109/PowerTech59965.2025.11180410.
[5] Abdolahi, M., Song, W., & Yazdani-Asrami, M. (2026). Intelligent condition monitoring of power cables using advanced machine learning models. Results in Engineering, 29, 108371. https://doi.org/10.1016/j.
[6] M. Abdolahi, W. Song and M. Yazdani-Asrami, “ML-Assisted Ageing Classification of XLPE Power Cables Using an Adaptive Neuro-Fuzzy Inference System,” 2025 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP), Manchester, United Kingdom, 2025, pp. 888-891, doi: 10.1109/CEIDP61707.2025.
[7] T. Spina et al., “CABLEGNOSIS Project: Ageing Studies on Insulating Materials and Superconducting Wires for Cable Applications,” in IEEE Transactions on Applied Superconductivity, vol. 36, no. 5, pp. 1-5, Aug. 2026, Art no. 4801205, doi: 10.1109/TASC.2026.3660644 CABLEGNOSIS Project: Ageing Studies on Insulating Materials and Superconducting Wires for Cable Applications | IEEE Journals & Magazine | IEEE Xplore


