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Record W4417201671 · doi:10.1109/tpel.2025.3642727

Near-Magnetic Interference in Rogowski Coil for Power Electronic Applications: Mechanism and Mitigation

2025· article· W4417201671 on OpenAlexafffund
Yulei Wang, Rachit Pradhan, Di Wang, Shreyas B. Shah, Linke Zhou, Jiaming An, Mohamed Abdelmagid, Giorgio Pietrini, Ali Emadi

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2025
Typearticle
Language
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsMcMaster University
FundersMitacs
KeywordsRogowski coilInductanceElectromagnetic coilOffset (computer science)Interference (communication)Robustness (evolution)Electromagnetic interferenceElectromagnetic compatibilityBandwidth (computing)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.225
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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