A High Frequency Injection Technique With Modified Current Reconstruction for Low-Speed Sensorless Control of IPMSMs With a Single DC-Link Current Sensor
Bibliographic record
Abstract
This paper proposes a high frequency injection technique applied for low-speed sensorless control of interior permanent magnet synchronous machines (IPMSM) with a single dc-link current sensor. The three-phase currents, which can be reconstructed by the measurement of a dc-link current sensor, always suffer from the immeasurable regions. The most challenging problem exists in the low modulation region, where no phase current can be measured by the dc-link current sensor, and it is rarely solved without PWM modifications. This paper proposes a six-direction square wave high frequency injection method to extend the voltage vector to the measurable region to achieve the three-phase current reconstruction and saliency-based sensorless control without modifying the space vectors of the pulse width modulation (PWM). In addition, the paper comes up with a modified reconstruction scheme to reduce the reconstruction error and improve the accuracy of the position estimation. This low-speed sensorless strategy using a single dc-link current sensor is implemented in dSpace platform and the performance is evaluated by experiments.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".