Location of DC line faults in conventional HVDC systems with segments of cables and overhead lines using terminal measurements
Bibliographic record
Abstract
Summary form only given. This paper presents a novel algorithm to determine the location of DC line faults in an HVDC system with a mixed transmission media consisting of overhead lines and cables, using only the measurements taken at the rectifier and inverter ends of the composite transmission line. The algorithm relies on the travelling wave principle, and requires the fault generated surge arrival times at two ends of the DC line as inputs. With accurate surge arrival times obtained from time synchronized measurements, the proposed algorithm can accurately predict the faulty segment as well as the exact fault location. Continuous wavelet transform coefficients of the input signal are used to determine the precise time of arrival of travelling waves at the DC line terminals. Two possible input signals, the DC voltage measured at the converter terminal and the current through the surge capacitors connected at the DC line end, are examined and both signals are found to be equally effective for detecting the travelling wave arrival times. Performance of the proposed fault-location scheme is analyzed through detailed simulations carried out using the electromagnetic transient simulation software PSCAD®. The impact of measurement noise on the fault location accuracy is also studied in the paper.
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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 | 0.001 |
| 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) | 0.001 | 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".