Saliency-based Speed Sensorless Control of Single-Inverter Dual Induction Machines using Reduced Amount of Current Sensors
Bibliographic record
Abstract
Parallel supply of dual-motors by single-inverter is a frequent practice, especially in traction applications. Model-based sensorless drives mostly rely on four current sensors, two of which attached to each motor. In the medium to high speed range, these model-based strategies can calculate the flux and torque share of each motor since the inverter output voltage is relatively linear. However, zero electrical speed operation turns out to be unstable as the system becomes unobservable. This area can be covered by injection strategies. This paper applies the voltage step excitation sensorless concept to dual-motor drives, which has not been researched in literature to the best of author's knowledge. Besides, a novel current sensor configuration is presented, using only three (instead of four) current sensors. Thereby, phase A and B currents of one motor (M1) will be measured, while the third sensor is attached to phase C of M2. As will be shown, new sensor arrangement allows for separation of individual machines inherent saliencies and thus delivers information of both machines rotor position, enabling a correct motor torque/flux share calculation by means of FOC equations. Experimental results prove the functionality of the new sensor configuration and voltage step excitation strategy applied to two parallel-connected induction motors.
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 | 0.000 |
| 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".