Efficient Modeling of Adjustable Speed Drive Systems for Offline and Real-Time EMT Simulators
Bibliographic record
Abstract
Adjustable speed drives (ASDs) comprised of inverter-driven induction machines are now widely used in many applications. Design and analysis of such systems require accurate and efficient models of induction machines (IM) and voltage source inverters (VSI). Such studies are often done in real-time or offline multiple times for tuning/optimizing controller parameters. Usually, simulations of such systems are carried out using detailed switching models of VSI and qd IM models, which are readily available as built-in library components in many electromagnetic transient (EMT) simulation programs. However, detailed switching models of converters with qd IM models typically require interfacing snubbers and small time steps, making them computationally expensive. As an alternative, this paper extends the prior work and presents an efficient simulation method using the average-value model of VSI and decoupled constant-parameter voltage-behind-reactance (DCPVBR) model of IM. Simulation studies carried out using MATLAB-Simulink and real-time OPALRT5700 simulator demonstrate the computational advantages of the proposed method over existing approaches.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".