Metrology and the elctric power grid modernization
Bibliographic record
Abstract
Metrology, as the science of measurement, is the very basis for acquiring scientific knowledge. In electric power systems, measurements of electrical and non-electrical quantities are necessary for monitoring, control, protection, and sage and reliable operation. Another significant application is revenue metering for industrial, commercial and residential customers. In today’s interdependent world, ensuring uniform metrology inside and across national boundaries is an important enabling factor of national and international trade, including the electrical energy trade. The introduction of distributed power generation, renewable energy resources, and deregulation of electric power utilities has in many countries, been transforming the electric power grids. We are witnessing exciting developments in metrology, on which the Smart Grid is based. This includes smart metering, synchro phasor and frequency measurements, wide-area protection, wide-area situational awareness, digital substations, energy storage and other evolving power system technologies. The proliferation of harmonics in the grid has been increasing over the last decades. With the prominent role of power electronics in new Smart Grid developments, this trend continues. The increased penetration of electric vehicles (EV) and the EV infrastructure emphasizes the necessity for wide frequency band measurements of distorted waveforms. New instrumentation and measurement methods for industrial applications at high-voltage and high-current levels are being developed. The digital measurements of voltage, current, phase, frequency, power and energy are continually improving. This is pushing the boundaries of the high accuracy measurements needed for calibrations. The role of National Measurements Institutes in providing the highest accuracy measurement standards and traceability to SI units is essential. The SI is undergoing profound changes, and the new revised SI is about to be adopted. Metrology has a key role in the research, development and innovation of evolving electric power grid. In the words of Lord Kelvin: “If you cannot measure it, you cannot improve it.”
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".