Performance analysis of grid connected induction motor using floating H-bridge converter
Bibliographic record
Abstract
Floating capacitor voltage source power electronic converters can be used as series voltage compensators for grid supplied induction motors to improve their steady-state operating performance, namely: actively reducing the motor power losses over its entire load range; lower the motor's operating temperature to improve lifetime expectancy; the voltage supplied to the motor can be used to avoid derating the motor power rating. These features are especially useful when the motor is connected to a grid whose nominal voltage differs from the machine's rated value or that may fluctuate over time (sag or swell). By injecting a voltage in series with the grid supply, floating capacitor converters can set the motor voltage at a fixed desired value, above or below the grid voltage, under transient or continuous steady-state conditions, and over the entire range of the motor load. These features allow the losses in the motor and power electronics to be controlled, e.g. minimized, hence improve the motor and system efficiencies. For applications where frequency control is not required, the proposed power electronics is more energy efficient; hence a viable cost effective solution. Experimental tests are used to compare the proposed system with several alternatives, using performances such as: power losses and efficiency of the motor, power electronics and the system as a whole.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".