Setting transmission line out-of-step relays in complex power systems
Bibliographic record
Abstract
Large disturbance in power systems such as network faults, line switching, generator disconnections and rejection of large loads may result in a transient mismatch between the power generation and consumption that could lead to oscillations in the synchronous machine rotor angles. These oscillations, (referred to as power swings) can be damaging if they result in out-of-step (OOS) conditions. Power swings can result in unwanted relay operations that may further aggravate the disturbance leading to blackouts as the August 10, 1996, Western North America blackout. OOS protection can be employed to detect such power swings and strategically block certain protection elements to avoid undesired operations or trip and disconnect the network at specific points to minimize outages. Typically, OOS protection examines the measured impedance at the relay location to detect power swings and determine whether they result in OOS conditions. The settings for OOS relays are specific to the power system and relay location. The current practice of determining OOS relay settings is not an exact science and requires engineering judgement. There may be scenarios where a relay cannot perfectly protect against all out-of-step events without compromising the security. In this thesis, a general methodology which can be used to identify OOS relay settings is presented. It exploits the OOS relay models available in dynamic simulation programs to measure swing speeds and determine preliminary relay settings which are further refined to fit specific conditions. A test system developed in PSSE is used to test the proposed methodology with simulations performed in PSSE software. The settings found from the proposed methodology are further tested by applying to a detailed electromagnetic transient simulation performed in PSCAD software.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".