@inproceedings{ff52d70fc1cb49759854666ee4eac843,
title = "Online parameter identification for multiport magnetic networks energy routers",
abstract = "A bstract-Multiport magnetic network energy router (MMNER) is attractive for DC micro grids with the advantages of high-power density and electrical isolation. Parameter identification is essential for MMNER since the parameters may change with aging and thus deteriorates the output performances. This paper proposes a forgetting factor recursive least squares (FFRLS) method-based parameter identification. By constructing the model system equations with the introduction of forgetting factor and solving them using the recursive least squares method, the equivalent connection inductance parameter identification results are finally obtained. The proposed FFRLS method can effectively identify parameters of MMNER, and accordingly improve the reliability of power control for MMNER. Besides, the simulation results show that the error of identified parameters (average value) are all within 1\%, proving the effectiveness of this method.",
keywords = "inductance, renewable energy sources, parameter estimation, simulation, power control, aging, mathematical models, real-time systems, reliability, least mean squares methods",
author = "Sayed Abulanwar and Zhiyong Li and Fujin Deng and Yongqing Lv",
year = "2024",
month = dec,
day = "4",
doi = "10.1109/icpre62586.2024.10768295",
language = "English",
series = "International Conference on Power and Renewable Energy (ICPRE)",
publisher = "IEEE",
pages = "1474–1479",
booktitle = "2024 The 9th International Conference on Power and Renewable Energy (ICPRE)",
}