Constant-Parameter Discretized State-Space Model of Saturable Induction Machines for Fixed Time-step Simulations
Bibliographic record
Abstract
Design and analysis of today's power systems are highly dependent on various simulation programs and platforms (including real-time FPGA-based hardware-in-the-loop simulators), that require accurate and numerically efficient models of all power system components. Induction machines are widely used in power systems and have numerous well-documented qd-models in the literature that also include magnetic saturation. However, such models are nonlinear and generally, when discretized, will have variable parameters, which makes their use costly in transient simulation studies of large-scale power systems. This paper proposes a constant-parameter discretized qd state-space model for induction machines including the main flux saturation. The presented formulation achieves constant-parameters, and therefore, the proposed model is shown to have superior numerical efficiency with minimal compromise in numerical accuracy. It is envisioned that the presented model may be a suitable for large-scale power systems studies in simulators with small time steps.
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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) | 0.001 | 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".