Speaker
Description
DC Current Transformers (DCCTs) are essential instruments for non-interceptive beam current measurement in particle accelerators. The zero-flux modulation principle demands exceptional symmetry between paired magnetic cores to achieve sub-$\mu$A offset stability. Conventional core matching based on static magnetic parameters ($B_r$, $H_c$) provides only an engineering approximation, as it neglects the dynamic magnetization behavior under AC modulation. This paper presents a novel approach employing unsupervised machine learning techniques---K-Means, DBSCAN, and Agglomerative Hierarchical Clustering---applied to dynamic magnetic parameters ($\delta$, $B_r$, $H_c$, $H_m$) measured at \SI{20}{kHz} sinusoidal excitation for 20 Co-based amorphous cores. Principal Component Analysis (PCA) reduces the feature space while preserving 98.9\% of variance. Comprehensive evaluation identifies optimal core groupings with silhouette scores exceeding 0.73, enabling physically rigorous pairing that accounts for eddy current losses and domain wall dynamics during actual DCCT operating conditions.
Footnotes
† huangwei@ihep.ac.cn
Funding Agency
- This work is supported in part by National Natural Science Fund (No. 12275294).
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