Method for accurately measuring the power‐frequency parameters of EHV/UHV transmission lines
Bibliographic record
Abstract
Power‐frequency parameters of long‐distance extreme‐high‐voltage (EHV) and ultra‐high‐voltage (UHV) transmission lines are the basis of power system modelling and analysis, but these parameters are especially difficult to accurately measure due to the reactance‐dominated nature and strong external interference. An accurate power‐frequency parameter measurement approach is proposed in this study. The idea is to utilise frequency response curve to indirectly extrapolate transmission line parameters at power frequency. As no direct measurement is conducted at power frequency, the potential power‐frequency interference is avoided. A resonance‐based measurement method is employed to eliminate the effect of large impedance phase angle of EHV/UHV systems and identify positive‐ and zero‐sequence resistance with enhanced accuracy. In order to validate the effectiveness of the proposed approach, a prototype instrument was developed and used to measure power‐frequency parameters of a scale‐down laboratory transmission line system. Comparative results confirmed the accuracy priority of the proposed approach over existing power‐frequency measurement methods. In addition, the anti‐interference performance in terms of the impact of the parallel in‐service transmission line also indicates the proposed approach is more advantageous as it has lower capacity requirement on the measurement device.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".