Analyzing and modeling of a new resonance inverter for low power vehicular application
Bibliographic record
Abstract
In this paper we proposed a novel inverter to convert a DC voltage to a desired AC voltage. This inverter is designed for variable inductive loads and low output power applications. We proposed a novel simple PWM method which enables the inverter to keep the output current at six times more than the rated output current, without reducing output voltage value. In addition, based on a simple use of resonance in the circuit, the new inverter can raise the output voltage to extremely high amplitude for a relatively short time. These characteristics make the proposed inverter is useful for some industrial applications such as electrical vehicle. The new configuration of the circuit consists of a unidirectional switch, two inductors (to transfer energy), a fast diode and a capacitor. With very few elements, this system changes the input DC voltage to a desired sinusoidal AC voltage. Some advantages of this system are: number of power electronics devices is low - it only uses a simple switch; it does not need dead time, in spite of conventional inverters which need a dead time between switches to prevent from short-circuiting; volume of the circuit is very small; the total harmonic distortion (THD) is greatly reduced; and it works with high efficiency. Another advantage of this inverter is capability of the circuit in boosting or bucking the input voltage to a desirable output voltage without using any DC-DC converters. It is flexible in keeping output voltage constant when the output current is increased, and it can produce the extremely higher output voltage than input voltage. The main drawback of the method, compared with conventional voltage source inverters, is that it uses more energy storage elements in spite of its low volume of power. We used simulations to prove all these statements.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".