Application of Artificial Intelligence Methods in the Analysis of the Cyclic Durability of Superconducting Fault Current Limiters Used in Smart Power Systems

Sylwia Hajdasz, Marek Wróblewski, Adam Kempski and Paweł Szcześniak ()
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Sylwia Hajdasz: Institute of Automatics Control, Electronics and Electrical Engineering, University of Zielona Góra, 65-417 Zielona Góra, Poland
Marek Wróblewski: Institute of Control and Computation Engineering, University of Zielona Góra, 65-048 Zielona Góra, Poland
Adam Kempski: Institute of Automatics Control, Electronics and Electrical Engineering, University of Zielona Góra, 65-417 Zielona Góra, Poland
Paweł Szcześniak: Institute of Automatics Control, Electronics and Electrical Engineering, University of Zielona Góra, 65-417 Zielona Góra, Poland

Energies, 2025, vol. 18, issue 17, 1-29

Abstract: This article presents a preliminary study on the potential application of artificial intelligence methods for assessing the durability of HTS tapes in superconducting fault current limiters (SFCLs). Despite their importance for the selectivity and reliability of power networks, these devices remain at the prototype testing stage, and the phenomena occurring in HTS tapes during their operation—particularly the degradation of tapes due to cyclic transitions into the resistive state—are difficult to model owing to their highly non-linear and dynamic nature. A concept of an engineering decision support system (EDSS) has been proposed, which, based on macroscopically measurable parameters (dissipated energy and the number of transitions), aims to enable the prediction of tape parameter degradation. Within the scope of the study, five approaches were tested and compared: Gaussian process regression (GPR) with various kernel functions, k-nearest neighbours (k-NN) regression, the random forest (RF) algorithm, piecewise cubic hermite interpolating polynomial (PCHIP) interpolation, and polynomial approximation. All models were trained on a limited set of experimental data. Despite the quantitative limitations and simplicity of the adopted methods, the results indicate that even simple GPR models can support the detection of HTS tape degradation in scenarios where direct measurement of the critical current is not feasible. This work constitutes a first step towards the construction of a complete EDSS and outlines directions for further research, including the need to expand the dataset, improve validation, analyse uncertainty, and incorporate physical constraints into the models.

Keywords: superconducting fault current limiter; high-temperature superconducting; machine learning; artificial intelligence methods; predictive system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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