Closed-Loop Traveling-Wave Relay Testing (TWRT) using RTDS Real-Time Simulators
Bibliographic record
Abstract
This paper presents a methodology to perform closed-loop Traveling Wave Relay Testing (TWRT) for transmission lines in power systems using RTDS real-time simulator. The Traveling-Wave Relays (TWRs) have a high sampling rate, e.g., around 1MHz, therefore, one requirement for TWRT is that the simulator operates at a small time-step, i.e., in the order of one microsecond. The TW-based protection elements operate based on current and voltage TW signals that are derived from the High-Frequency (HF) response induced by faults on the transmission line. This paper shows that employing inaccurate line models in a simulator can result in incorrect TW signals. Therefore, another requirement for TWRT is to use accurate line models. Fulfilling these requirements requires high computational power and introduces challenges to real-time simulators for the TWRT application. This paper presents two real-time simulator approaches for TWRT: (i) an FPGA-based and (ii) a multi-core CPU-based approach. The proposed approaches are employed to test commercially available TWRs and the results are reported.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".