Electric Field Energy Harvesting From High-Voltage Power Lines for Consumer Batteryless Wireless Sensor Networks
Bibliographic record
Abstract
Leakage electromagnetic energy widely exists in the vicinity of high-voltage power lines. This work proposes a comprehensive electric field energy harvester, which can drive a commercial consumer-oriented Zigbee-based Wireless Sensor Platform (WSP). Electric field energy harvesting is selected as its energy density is about 60 uJ/m3 under 525-kV power lines, twice higher than that due to the magnetic field. To this end, a capacitive coupling model is studied to evaluate electric energy harvesters placed under high-voltage power lines, which is proven with good accuracy. A complete energy harvesting platform is developed, which contains a two plates-based energy harvester, a bridge rectifier, a storage capacitor, and an ultra-low-power comparator. Experimental verification shows that the proposed batteryless wireless sensing platform can operate every 40 s corresponding to 3.3 mJ of energy collected in this period under the 525-kV power lines. This electric energy harvesting approach is believed to have great potential for energizing wireless sensor networks under high-voltage power lines.
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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 | 0.001 |
| Open science | 0.000 | 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".