Accurate adaptive integration algorithms for induction machine drive over a wide speed range
Bibliographic record
Abstract
This paper presents three new architectures for designing accurate adaptive integration algorithms (AAIA) for quasi exact flux position and magnitude estimation of induction machines over a wide speed range. Pure integrators are in practice affected by DC-offset and DC-drift problems while estimating flux position and magnitude from the back electromotive force (emf). Modified integration algorithms based on low pass filter (LPF) or programmable LPF are also known to be affected by the cut-off frequency. The proposed architectures are based on the association of high pass filters (HPF) and pure integrators. DC-offsets and drift problems are eliminated by the HPFs before integration. The HPF characteristics are used for magnitude (gain) and position (angle) compensation. The HPF cut-off frequency can be chosen far from the inverse of stator time constant without affecting the estimation of low frequency signals resulting into good accuracy over a wide speed range. A strong agreement is observed between the simulation and experimental results that demonstrate the accuracy of the proposed architectures. The proposed AAIA can be used for any kind of induction machine (IM) since they are independent from the IM parameters. The AAIA can also be used to estimate stator flux in order to estimate the IM parameters online, as required for adaptive control in indirect rotor flux oriented control (IRFOC) schemes
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".