Experimental Identification of a Coupled-Circuit Model for the Digital Twin of a Wound-Rotor Induction Machine
Bibliographic record
Abstract
The development of monitoring and diagnostic methods for electrical machines requires the use of transient models capable of operating in real time and producing signal signatures with high precision. In this context, coupled-circuit models offer numerous advantages due to their speed of execution and accuracy. They have been successfully employed to create real-time digital twins of electrical machines. The main challenge of this modeling method lies in the preparation of the model, which involves numerous preliminary calculations and takes time to identify all its parameters. This is particularly due to the variation in inductances based on the rotor position. To determine these inductance values with great precision, the classical approach involves using finite-element field calculation software. However, the computation time quickly becomes an issue due to the large number of values to calculate and simulations to perform. This article introduces an innovative experimental approach to identify a coupled-circuit model and develop a digital twin of a wound-rotor induction machine. This method relies solely on simple electrical measurements and tests conducted at extremely low rotation speeds (1 rpm) to obtain inductance variations as a function of the rotor position. By employing this technique, the need for analytical models or finite-element field calculation simulations, which typically require precise knowledge of the machine’s geometry and materials, is circumvented. The measurement processing employs optimization methods to extract the inductances as a function of the rotor position, which are then used as input data for the coupled-circuit model. The final parameters are specific to each machine and replicate all its manufacturing imperfections such as eccentricity and geometric or winding defects. This experimental identification method significantly enhances the model’s accuracy and reduces the usually required preliminary calculation time in a finite-element-based identification process.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".