Calibrated Single-Contact Voltage Sensor for High-Voltage Monitoring Applications
Bibliographic record
Abstract
A single-contact voltage sensor designed for accurate measurements of ac voltages across a pair of conductors is described. The sensor design is motivated by remote monitoring applications where accurate voltage measurement of high-voltage transmission lines is required. The body of the sensor is electrically and mechanically attached to a single conductor: either the neutral or high-voltage conductor. A capacitive sensing plate attached to the sensor creates a capacitive voltage divider by using the stray capacitance to the noncontacted line. A very high-impedance buffer is used to measure the voltage across the divider output and estimate the line voltage. An important part of this paper includes a method of calibrating the sensor such that blind voltage measurements can be made without knowing the exact geometry of the conductors. Other important aspects of the design include a two-stage voltage divider for retaining accuracy and increasing the voltage range of the sensor. The work is supported by extensive numerical simulation models which were used to determine the optimum design for the sensing plate and to evaluate the sensitivity to different configurations including conductor spacing and the height above ground. For calibration values which are accurate to 1%, the line voltage can be measured with an accuracy of 10%. The paper describes the theory, design, and experimental verification of the sensor up to a line voltage of 7.5 kVrms.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".