Efficient Implementation of High-Fidelity Models of IPMSM Drive Systems in Offline and Real-Time Simulators
Bibliographic record
Abstract
Interior permanent magnet synchronous machines (IPMSMs) are widely used in industrial applications, electric propulsion and precision applications. Accurate and efficient simulations of IPMSM drives are essential for the design and tuning of such systems. Field-oriented control (FOC) is commonly used to achieve precise control by decoupling the stator currents into flux- and torque-controlling components. The FOC-based controllers, however, require addressing the nonlinear flux-current relationships in IPMSMs caused by saliency, magnetic saturation, and cross-coupling effects. Different methods, such as parameter estimation and look-up tables (LUTs) techniques, are used to model these nonlinearities, with LUTs offering accuracy but demanding substantial memory allocation. Alternatively, memory-compact models such as polynomial-based models are used when systems impose constraints on memory allocation. This paper presents the efficient implementation of both the LUT-based and polynomial-based IPMSM drive system models for offline and real-time simulators. The accuracy of the models is verified through offline studies performed in MATLAB/Simulink and OPAL-RT's OP5700 real-time simulator. The obtained results demonstrate the difference in system resource utilization, such as memory requirement and computational efficiency, when using LUT-based and polynomial-based IPMSM models.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".