Near-Magnetic Interference in Rogowski Coil for Power Electronic Applications: Mechanism and Mitigation
Bibliographic record
Abstract
Rogowski coil (RC), valued for its high bandwidth, galvanic isolation, and ease of integration, is widely employed in high-speed integrated power electronics. However, near-magnetic interference (NMI) arising from inductive coupling with adjacent current sources is often underestimated, which undermines the RC's robustness in crucial applications such as characterization, protection, and control. This paper first reveals the inevitability of NMI in RCs. Building upon this, the mutual inductance equation between the RC and external interference sources is derived in detail, establishing a solid theoretical foundation for efficient and quantitative interference evaluation. The derivation methodology is further generalized to arbitrary points in space, allowing for the quantitative analysis of the effect when the circuit under test (CUT) is offset from the RC's center. Theoretical analysis highlights the number of turns as a key factor, demonstrating that appropriately increasing it can effectively mitigate the influence of both external NMI and internal CUT eccentricity. The inherent design trade-off between measurement bandwidth and interference immunity is further analyzed. Design guidelines for establishing the upper and lower limits of the number of turns are derived, and the differential mode rejection ratio (DMRR) is introduced as an intuitive metric to quantify the interference suppression capability. The accuracy of the proposed NMI model and the effectiveness of mitigation strategies are corroborated by Ansys Q3D simulations and extensive frequency- and time-domain experiments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".