New Fault-Resistance Estimation Algorithm for Rotor-Winding Ground-Fault Online Location in Synchronous Machines With Static Excitation
Bibliographic record
Abstract
This paper presents a new algorithm for estimating the ground-fault resistance value in rotor windings. This new algorithm is an improvement of an online ground-fault location method previously presented. This location method is suitable for synchronous generators with static excitation, whose excitation field winding is fed by controlled rectifiers through an excitation transformer. The estimation of the fault resistance is obtained through the comparison between the third-harmonic voltage measured in a grounding resistance placed in the neutral of the excitation transformer and the third-harmonic voltage calculated by the algorithm. This latter variable is obtained with the dc component of the output voltage of the controlled rectifier and the ac supply voltage of this converter. The fault resistance value is used in the novel technique of online ground-fault location, and it allows improving the accuracy of the location of the defect. This new algorithm, integrated in the complete location method, has been tested with satisfactory results in a 5-kVA laboratory synchronous generator and in a 106-MVA hydro-generating unit.
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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 | 0.002 |
| 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 | 0.001 | 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".