Average-Value Modeling of Synchronous-Machine-Fed Thyristor-Controlled-Rectifier Systems
Bibliographic record
Abstract
Due to the repeated switching, the detailed switch-level models of electrical machines coupled with power-electronic converters are computationally expensive and hard to linearize for small-signal frequency-domain analysis. Average-value modeling (AVM) has become an effective tool for small-signal analysis of power electronic systems and time-domain transient studies where the details of switching are not important and can be neglected. Recently, a parametric AVM (PAVM) approach has been developed for machine/diode rectifier systems. This paper extends the parametric approach to the machine/thyristor-controlled-rectifier systems, where the thyristor firing may be referenced to either the line voltages or the rotor position. An analytical average model for this system is also developed based on the recently proposed constant-parameter voltage-behind-reactance synchronous machine model. The new PAVM is compared against the original switching system, as well as the analytical AVM. It is shown that the PAVM can accurately predict both small-signal characteristics and large-signal transients of the original switching system in light and heavy modes, which represents an advantage over the analytical models which are typically implicit.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".