Average-Value Modeling of Multi-Phase Machine-Converter Systems with Asymmetric Internal Faults
Bibliographic record
Abstract
Multi-phase machine-converter systems are being utilized in many advanced energy conversion systems, e.g., aircraft, marine and vehicular power systems, and renewable energy generation. Accurate and efficient models are essential for simulation, analysis and design of such systems. Recently, a parametric-average-value modeling (PAVM) technique has been presented for 12-pulse rectifiers connected to six-phase machines including several dominant harmonics (i.e. 5th, 7th, etc.). Although the presented PAVM allows fast simulations, it assumes symmetrical operation of the rectifier, which limits its application only to normal operation. In this paper, the PAVM methodology is extended to consider asymmetrical operation of the 12-pulse rectifiers that may occur due to internal faults of some of the switches. This is achieved by formulating the characteristic as well as non-characteristic (i.e. 2nd, 3rd, etc.) harmonics in both positive and negatives sequences in addition to the dc components appearing in ac voltages and currents. Using a case-study system consisting of a six-phase generator connected to a 12-pulse ac–dc rectifier, it is demonstrated that the proposed extended PAVM can accurately reconstruct the waveforms of the detailed switching model of 12-pulse rectifier in faulty conditions while allowing much faster simulations.
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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 | 0.001 |
| 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) | 0.001 | 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".