A New Method for Blocking Third-Zone Distance Relays During Stable Power Swings
Bibliographic record
Abstract
The improper operation of the third-zone distance protection has been attributed as one of the factors for power system blackouts. The third-zone protection could incorrectly operate during power-swing scenarios. This research proposes a fast and practical methodology using local measurements for blocking the third-zone distance relays during stable power swings. The proposed scheme is referred to as the BTZ scheme. The proposed method calculates the relative speed of a fictitious equivalent machine from the local relay measurements. If the relative speed goes through a zero crossing, the swing is classified as a stable power swing; whereas if the speed does not go through a zero value, then the swing is classified as an unstable power swing. The benefit of the proposed method is that it analyzes the power swings from a system stability point of view, and does not need rigorous offline simulation studies to determine the relay settings. The performance of the proposed method in this paper is compared with an industry-standard method (conventional double blinder method). As is well known, the settings of the blinder-based methods are system specific and need several simulations to arrive at the blinder values. Transient simulation studies on a three-bus system and a modified Western Coordination Council 9-bus system are used to test the performance of the proposed approach.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".