Fast Restarting of Free-Running Induction Motors Under Speed-Sensorless Vector Control
Bibliographic record
Abstract
In some applications of inverter-fed induction motor drive systems, a fast and smooth restarting of free-running motors may be required when their inverters are faced with short-term power interruptions. In this article, a novel restarting strategy is proposed, consisting of three steps. First, during the restarting process, the initial step requires the machine's speed estimation. To achieve this, a dc current injection-based method is used to estimate the rotor flux and subsequently the speed of the rotating machine. In this step, a filtering and a phase-locked loop are used to extract speed information. Next, to further improve the speed estimation accuracy, the speed-sensorless vector control with zero torque command is used. Finally, a full-order adaptive observer is utilized for speed estimation in the restored normal operation of the induction motor. Compared to existing/alternative methods, the proposed strategy much faster estimates the initial speed and is able to restore the normal smooth operation within 0.5 s. The effectiveness of the proposed method is demonstrated by simulation and experimental studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| 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".