Intelligent estimation of critical current degradation in HTS tapes under repetitive overcurrent cycling for cryo-electric transportation applications

• DOI: 10.1016/j.mtphys.2024.101365
• Publication Date: 2024-02-15T10:51:51Z
• Authors: Alireza Sadeghi; Shahin Alipour Bonab; Wenjuan Song; Mohammad Yazdani-...
• Keywords: Overcurrent

Overcurrent cycling refers to the procedure of imposing repetitive overcurrent superconducting tapes/devices for characterizing their critical current reduction. Characterizing behaviour Rare Earth Barium Copper Oxide (ReBCO) tapes is a crucial step in design process High Temperature Superconducting (HTS) devices. Multiple incidents during operation an HTS device can significantly decrease total current, leading potential quenches and failures. Data-driven models have been proposed literature estimate Critical Current Degradation Rate (CCDR) ReBCO under multiple scenarios. However, these methods exhibited notable errors range 8%–11%, estimation This paper proposes method based on Artificial Intelligence (AI) techniques aimed at challenges conventional CCDR estimation. Different AI-based were proposed, tested, compared show effectiveness intelligent approach, including Support Vector Regression (SVR), Decision Tree (DT), Radial Basis Function (RBF), Fuzzy Inference System (FIS). Experimental data values subjected cycles employed this investigation. The results demonstrated that Mean Relative Error (MRE) SVR 23%, DT model approximately 0.61%, MRE FIS well above 0.06%, value RBF about 1.1 × 10−6%. Moreover, AI offer fast test times, ranging from 1 11 ms. These findings highlight using enhance accuracy risks associated with events.