Detecting Broken Conductor Faults in the Presence of Inverter‐Based Resources
Bibliographic record
Abstract
ABSTRACT The increasing number of inverter‐based resources (IBR) in the power system and the fast response of IBRs during faults impose new challenges for protection. An open circuit (OC) fault can be the result of a breaker malfunction or a broken conductor, where the broken conductor fault can occur with or without a series arc. It is essential to develop a fast broken conductor fault detection method in the presence of IBRs. An undetected broken conductor fault can degrade power quality, cause local outages and forest fires, and cause personnel injury if conductors contact the ground. Existing broken conductor fault detection methods typically use a measure of current imbalance; however, these methods can be inaccurate due to the current imbalance not being local to the faulted line. This paper proposes a method using current magnitudes and angles to detect a broken conductor fault with and without a series arcing event when the local generation is supplied by grid‐forming (GFM) and grid‐following (GFL) IBRs. The proposed broken conductor fault detection method without arcing looks for a decrease in phase current, an increase in zero‐crossing events, and an impedance angle that falls in a capacitive window. The proposed broken conductor fault detection logic with arcing alerts for a decrease in phase current and impedance angle over a predefined series arcing window. Time domain simulation studies are performed in PSCAD/EMTDC to evaluate the effectiveness of the proposed broken conductor for both GFM and GFL IBRs in approximately one fundamental cycle.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".