Fast adaptive schemes for tracking voltage phasor and local frequency in power transmission and distribution systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Real-time measurements of voltage phasor and local frequency deviation find applications in computer-based relaying, static state estimation, disturbance monitoring and control. Two learning schemes for fast estimation of these basic quantities are proposed. The problem was approached from a system identification perspective, in opposition to the well-established extended Kalman filtering (EKF) technique. It is shown that, from a simple nonlinear model of the system voltage which involves only two parameters, the recursive least squares (RLS) and the least mean squares (LMS) algorithms can each provide dynamic estimates of the voltage phasor. The finite derivative of the phase deviation, followed by a moving-average filter, then leads to the local frequency deviation. A constant forgetting factor included in these algorithms provides both fast adaptation in time-varying situations and good smoothing of the estimates when necessary.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it