High-Frequency Modeling of the Long-Cable-Fed Induction Motor Drive System Using TLM Approach for Predicting Overvoltage Transients
Bibliographic record
Abstract
Induction motor drive systems fed with cables are widely used in many industrial applications. Accurate prediction of motor terminal overvoltage, caused by impedance mismatch between the long cable and the motor, plays an important role for motor dielectric insulation and optimal design of dv/dt filters. In this paper, a novel modeling methodology for the investigation of long-cable-fed induction motor drive overvoltage is proposed. An improved high-frequency motor equivalent circuit model is developed to represent the motor high-frequency behavior for the time- and frequency-domain analyses. The motor equivalent circuit parameters for the differential mode (DM) and common mode (CM) are extracted based on the measurements. A high-frequency cable model based on improved high-order multiple-$\pi$sections is proposed. The cable model parameters are identified from the DM impedances in open circuit (OC) and short circuit (SC). To obtain a computationally efficient solution that could potentially be integrated with the motor drive controller, the system equations are discretized and solved using transmission-line modeling (TLM) approach. The proposed methodology is verified on an experimental 2.2-kW ABB motor drive benchmark system. The motor overvoltage transients predicted by the proposed model is in excellent agreement with the experimental results and represents a significant improvement compared with the conventional models.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".