Considering Power Losses and Voltage Drop in Average-Value Modeling of Voltage Source Inverters in Simulations of AC Machine Drives
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Bibliographic record
Abstract
Voltage-source inverters (VSIs) are used extensively in AC machine drive applications. Detailed switching models of VSIs offer accurate but computationally expensive simulations. Average-value models (AVMs) of VSIs are often used for fast and efficient simulations in electromagnetic transient (EMT) programs. However, most conventional AVMs do not consider power losses. This paper extends the previous work on representing the losses in parametric AVMs of VSIs by including the overall losses that are frequency- and current-dependent, as well as voltage drop due to the ON -state resistance of switches. Several new lossy AVMs (LAVMs) and their equivalent circuits are presented. The losses may be implemented on the DC side, AC side, or distributed between the DC and AC sides. The proposed LAVMs are demonstrated on an induction machine drive for which the losses are determined experimentally. The proposed LAVMs exhibit exceptional accuracy in capturing the effects of losses and voltage drop compared to experimental results.
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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) | 0.000 | 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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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