Multiparameter Estimation Accuracy Improvement for PMSMs Using a Constriction Coefficient-Based Particle Swarm Optimization
Bibliographic record
Abstract
Accurate estimation of electrical parameters is important for high-performance control of permanent magnet synchronous machines (PMSMs). This paper proposes a novel non-invasive multi-parameter estimation method for PMSMs based on a constriction coefficient-based particle swarm optimization (CCPSO) algorithm which accurately estimates the electrical parameters of the machine including its stator winding resistance, permanent magnet (PM) flux linkage, and dq-axis inductances. Voltage source inverter (VSI) non-linearity due to the dead-time effect is compensated, and magnetic saturation is modeled through polynomial functions representing dq-axis inductance variations with currents. Furthermore, a method for decoupling the parameters in PMSM voltage equations is proposed to reduce both the cross-coupling effects between electrical parameters and the computational burden. Three main objective functions are defined and implemented using the CCPSO algorithm to track the parameters and enhance the estimation accuracy. The proposed method is validated through experiments on a laboratory 4.25-kW interior PMSM (IPMSM) over a wide range of operating speeds and loads. Finally, the estimated parameters from the developed CCPSO algorithm are compared to the results from the conventional least squares (LS)-based estimation to justify the higher accuracy of the proposed CCPSO approach.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".