Average-Value Modeling of Voltage-Source Inverters with Parametric Losses for AC Machine Drives
Bibliographic record
Abstract
AC machine drives with voltage-source inverters (VSIs) are extensively utilized in various applications. Average-value models (AVMs) of VSIs are used to facilitate fast and efficient simulations of such systems in electromagnetic transient (EMT) programs. However, the conventional existing AVMs do not consider losses in VSI operation. This paper presents a methodology to implement a lossy AVM (LAVM) and proposes three equivalent circuits for possible implementation. The LAVM considers conduction and switching losses, which also depend on operating conditions such as frequency and current. Depending on the LAVM interfacing needs, the losses may be implemented on the DC-side, AC-side, or split between DC and AC sides. The proposed methodology is demonstrated on an induction motor drive for which the losses are extracted experimentally and then fitted into the LAVMs. The proposed LAVM is shown to improve the accuracy of capturing the losses compared to the conventional detailed switching model and AVM of VSI driving the induction motor over a wide range of operating conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".