MV Motor Optimization: Copyright Material IEEE, Paper No. PCIC-2018-29
Bibliographic record
Abstract
This paper presents two alternatives to make choosing the appropriate cost-effective AC machine for a given application easier. One option increases the short circuit capacity (SCC) at the motor bus; the other uses a synchronous machine (SM) in lieu of an induction machine (IM). Both methods have the potential to yield higher efficiency designs with better transient ride-through capability. There are well-defined relationships between applied voltage, applied current, and output torque. In short, higher voltages and/or currents allow a rotating machine to develop more torque. For obvious reasons, this relationship becomes extremely important for the transient performance associated with direct-on-line (DOL) starting. Adding more SCC minimizes the voltage drop, yielding more efficient and less costly designs. Over the life of a typical machine (e.g. 5000 HP IM), even a relatively small efficiency improvement realizes significant savings - enough to purchase the medium voltage (MV) distribution equipment. At 10 000 HP, the efficiency gains achieved through use of a SM instead of an IM are even greater, yielding more savings. As an added benefit, the increase in SCC leads to a more stable system, allowing the motor to ride through low voltage fault conditions that might otherwise have caused a trip. This in turn means more consistent throughput and a more profitable facility.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".