Efficiency Estimation of the Induction Machine by Particle Swarm Optimization Using Rapid Test Data With Range Constraints
Bibliographic record
Abstract
Temperature rise of an induction machine increases losses and decrease in the efficiency. The temperature rise depends on the machine design. It takes several hours after starting a machine to reach to thermal stability. In most of the in situ efficiency estimation methods, it is required to obtain the operating data of the machine at a thermally stable condition, which needs a long running time of the machine. In this paper, a method based on a particle swarm optimization (PSO) algorithm is proposed, which can estimate the machine efficiency at different loads with thermal stability. The machine operation data at the first 30 min after the start rather than data at a thermal stability condition are used in the method. The proposed algorithm utilizes two approaches to predict a full-load temperature at a thermally stable condition. The first approach is based on the insulation class of the machine and uses the equivalent circuit. The second approach is based on the trend of the temperature rise in the first 30 min of running the machine after the start. Furthermore, a method is proposed to narrow the parameters range, which helps the PSO to converge to the right answer. All results are validated by the experimental results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".