Bibliographic record
Abstract
The research programme presented in this thesis terminates the first phase in the development of a new and accurate model for transient analysis of induction motors in the phase domain. Modelling the induction machine variables in the phase domain required a new model which when tested in similar conditions with existing models would give comparable results in both transient and steady-state studies. This new model has been developed, and essentially it differs from traditional models in that it works directly with the machine variables such as currents and voltages directly in the phase domain instead of the dqO coordinates. This required the solution of a series of first order differential equations with time-varying coefficients. The solution method is based on the discretization of the differential equations with the use of the trapezoidal rule of integration. The new model has been used to develop a computer program for transient and steady-state analysis of induction motors. The new phase domain transient model (PDTM) requires a number of circuit parameters of the induction motor that are not normally supplied by the manufacturer. Consequently, modifications were performed on a computer program that calculates the parameters of the standard 60-Hz equivalent circuit from starting and steady-state characteristics of the motor to obtain the circuit parameters of the PDTM. The results from the PDTM compare favourably tested with those obtained from the electromagnetic transient program (EMTP) which uses conventional dqO coordinates to model the induction motor.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".