Controlled Switching of a 1200 MVA Transformer in Manitoba
Bibliographic record
Abstract
This paper describes the application of a controlled switching device to energize a 500 kV/230 kV/46 kV, 1200 MVA autotransformer in Manitoba. The development of a detailed electromagnetic-transient (EMT) simulation model of the transformer formed the basis of the work described. The transformer model includes an accurate representation of hysteresis and remanance, which was validated using recorded waveforms and manufacturer data. This paper discusses a feasibility study conducted using this transformer model to evaluate the suitability of controlled switching to energize this transformer. A series of real-time hardware in the loop tests was conducted using a real-time simulator as part of precommissioning tests. The real-time simulation (RTS) tests and phase 1 of commissioning tests conducted in October 2014 showed that the controlled switching device would produce inaccurate residual flux estimates when it integrates the secondary voltage waveforms of a capacitive voltage transformer. Based on these RTS test results and EMT simulations, the installation of a wound potential transformer (PT) was recommended. This paper also discusses simulation studies conducted to manually program the controller until the PT was commissioned. During phase 2 of testing conducted in October 2015, a wound PT was commissioned and the controller was tested. The recorded waveforms showed good agreement with simulation results. The controller has accurately predicted residual flux and minimized inrush currents during transformer switching events.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".