Neural network based robust optimal energy control of pulse width modulation‐inverter fed motor driving pump
Bibliographic record
Abstract
Abstract This paper revisits loss model control (LMC) of the 3‐phase induction motor (IM) and presents a robust LMC algorithm for medium‐sized pump drives. Compared with other power loss reduction algorithms for IM, the presented one has the advantages of fast and smooth flux adaptation, high accuracy, and versatile implementation. An improved loss‐model for IM drive has been developed. The model considers the surplus power loss caused by inverter voltage harmonics and magnetic saturation using closed‐form equations. Further, the resistance‐temperature change is considered by a first‐order thermal model. To determine the optimal flux level that achieves maximum drive efficiency, an artificial neural network (ANN) controller is synthesised and trained offline. The voltage and speed control loops are connecting via the stator frequency to avoid the possibility of excessive magnetization. Beside, the obtained thermal information enhances motor protection and control. These together have the potential of making the proposed algorithm robust and reliable. The system reliability is investigated and assessed in terms of energy saving using ramp start/stop. Theoretical analysis, computer simulations, and experimental studies are performed on 5.5 kW variable speed water pump using the proposed control. The test results are provided and discussed to validate the effectiveness.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".