Development of interactive tutorial tool for simulation and identification of electrical machines and transformers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The paper focuses on the development of a Matlab based interactive and tutorial tool for simulation and parameters identification of electrical machines, transformers and several other dynamic systems. The proposed software allows predicting the steady-state and dynamic performances of three-phase induction and synchronous machines, DC machines in both motor and generator modes, three-phase transformers and several other dynamic systems. A given machine under study is formatted in state space models. This allows performing various standard and non-standard tests. For linear and nonlinear deterministic machine models, linear and nonlinear deterministic predictors (Euler method and fourth order Runge-Kutta) are used, while the classical linear Kalman Filter (LKF) and Unscented Kalman Filter (UKF) are applied for the state estimation of linear and nonlinear stochastic machine models respectively. The availability of several optimization approaches for parameters identification experiences offers to users a great flexibility and opportunity to compare their robustness.
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.
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 it