A Voltage-Behind-Reactance Induction Machine Model for the EMTP-Type Solution
Bibliographic record
Abstract
Recently, there has been renewed interest in modeling of electrical machines for the electro-magnetic transient program (EMTP)-type programs, with the goal of improving the machine- network interface. In this paper, we present a new voltage-behind- reactance induction machine model for the EMTP-type solution and power system transients. In the proposed model, the stator circuit is represented in abc phase coordinates and the rotor subsystem is expressed in qd arbitrary reference frame. Similar to the recently proposed synchronous-machine voltage-behind-reactance model and the established phase-domain model, simultaneous solution of the machine-network electrical variables is achieved. Efficient numerical implementation of the proposed model is presented, in which one time-step requires as little as 108 flops, taking 1.6 mus of CPU time. Case studies of induction machine start-up transients demonstrate that the proposed model is more accurate and efficient than several existing EMTP machine models.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".