Vacuum-Packaged Piezoelectric Energy Harvester for Powering Smart Grid Monitoring Devices
Bibliographic record
Abstract
This paper presents an analytical method for the design and power optimization of vacuum-packaged piezoelectric energy harvesters. It is shown that the maximum power point of a vacuum-packaged energy harvester is different from the conventional one which occurs when the electrical damping ratio equals to its mechanical counterpart. Also, it is shown that the captured power by a vacuum-packaged energy harvester is highly sensitive to the vibration frequency due to very low-mechanical damping ratio, e.g., up to 50% power drops corresponding to 2% deviations in the frequency. The analysis and design are performed in the context of an ac-line magnetic field energy harvester in which the line frequency is also fixed and this energy harvester is useful for developing the self-powered wireless monitoring devices. Furthermore, the vacuum-packaged devices are inherently robust against dust storm and icing phenomenon, which occur for overhead power lines. The proposed analytical method is established based on simplified assumptions and then an accurate method is developed for the analysis of vacuum-packaged devices. Obtained theoretical results are verified in the laboratory through a prototype of the vacuum-packaged piezoelectric device, which captures up to 90 μW from a 10-A line current.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".