Characteristic Modes Analyses for Misalignment in Wireless Power Transfer System
Bibliographic record
Abstract
This paper presents a novel method for assessing misalignment scenarios in Magnetically Coupled Resonant Wireless Power Transfer (MCR-WPT) system through measurements using Characteristic Modes Analysis (CMA). Based on the metric parameters of Characteristic Modes (CMs), including resonant frequencies, magnetic field plots, and current distributions, the dominant CM is identified. Its impact on the system’s total power is evaluated and compared with the system’s behavior in proximity to resonance. To establish a comprehensive and concise reference delineating the applicability of CMA in the field of Wireless Power Transmission, this contribution conducts a detailed investigation about the various types of unexpected misalignments between the Transmitting (Tx) and the Receiving (Rx) coils due to typical movement of the device or the user from both analytical and experimental perspectives. Besides, the various misalignment scenarios were systematically and progressively explored, accounting for both minor and significant degrees of variation. The proposed MCR-WPT system consists of two identical loops characterized by a singular turn, a radiusRof 0.0625λ and a widthwof 0.025R. Notably, results based on CMA guided by The Coupled Mode Theory (CMT) demonstrate a great affinity with the measurements of the fabricated prototypes. Consequently, this marks the inaugural confirmation of CMA’s applicability in the field of Wireless Power Transmission, which opens avenues for improving PTE using previous research findings deploying principles of selective excitation through CMA concepts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".