Bibliographic record
Abstract
In this paper a real time digital simulator (RTDS) is used to develop and verify protective relay algorithms by modeling the actual protective relay as well as the power system in one simulation. The real time operation of the simulator provides a time and personnel efficient environment for the work. This allows the models to be subjected to exhaustive test case scenarios before final implementation of the protective relay algorithm in the development platform. Since the developmental and final hardware implementations of the relay will be tested with the real time simulator, the use of the RTDS during early design stages also allows one simulation tool to be used throughout the process. Regression testing can be automated using the script features of RSCAD. The scripted cases can be run during non-work hours to minimize personnel requirements and maximize the simulator usage. This paper describes the technique used to model an impedance relay using the RTDS software and hardware. The relay model included a mho characteristic polarized with positive sequence memory voltage Andrichuk and Alexander (1), Roberts and Schweitzer (2). Phase selector logic, loss of potential logic and direction control logic, all of which are essential in a commercial relay (2), were not implemented here to limit the modeling complexity to the basic requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".