Parameters Identification of Induction Motor with Hybrid Metaheuristic Algorithm: Equilibrium Slime Mould
Bibliographic record
Abstract
The identification of electrical and mechanical parameters is a crucial step in the modeling and control of industrial electric motors.Incorrectly identified parameters or those estimated with considerable error can lead to instability or biased control of the system.In this paper, we present a study to identify the electrical and mechanical parameters of an induction motor (IM) using two recent metaheuristic techniques: the Slim Mold Algorithm (SMA) and the Equilibrium Optimizer (EO).A hybrid algorithm, the Equilibrium Optimizer-Slim Mold Algorithm (EOSMA), combining the advantages of both techniques, is proposed and compared to other methods to demonstrate its effectiveness.The identification of the IM parameters is based on the optimization (minimization) of an objective function, which measures the error between the electrical quantities (stator current and motor speed) obtained from the simulation of a mathematical model and those measured during an experimental test.The results show that the electrical parameters identified by the hybrid EOSMA algorithm are more accurate than those obtained with the other two techniques in terms of convergence and precision, thus validating the effectiveness of the hybrid method.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".