Advanced AC-AC, AC-DC and DC-DC power electronics converters for smart grid
Bibliographic record
Abstract
Converters and controllers of power electronics are the lifeblood of many emerging technologies in manufacturing, information technology and more. This thesis investigates the design, analysis, and implementation of advanced AC-AC, AC-DC and DC-DC power electronics converters for high voltage gain and voltage sag/swell compensation in power grids. Firstly, direct single-phase AC-AC converter by using an auto transformer approach and impedance network is presented. It has buck-boost operation in a single-stage direct AC-AC conversion. The high voltage gain is enabled through magnetic coupling and turn ratios of coupled transformer. Notably, it employs a coupled transformer, achieving high voltage gain while maintaining an optimal switching duty ratio, with a 1:1 unity turn ratio. Secondly, AC-DC converter with three output terminals for use in the DC applications is explained. This converter converts an input AC voltage into two output DC voltages. The output DC voltages can be obtained higher or lower than the input AC voltage. The proposed converter with four active switches is analyzed in detail, and control strategies are developed. Finally, a switched-inductor A-source DC-DC converter with an impedance network is presented. The operations of topology are based on the autotransformer technique, which are effective for a wide range of applications due to better DC voltage gain. This network achieves higher voltage gain with less switching duty cycle by using a minimal turns ratio rather than other magnetically coupled impedance source networks. The previous converters usually have high total harmonic distortion (THD) due to discontinuous input current and are not able to operate in buck-boost mode simultaneously. Integrated circuits are becoming smaller, but existing power electronics converters are still inefficient, large, and expensive. Thus, proposed converters have simple structures, operations and share a common ground between the input and output, which enhances the reliability of the power conversion systems. Experimental results are obtained to validate the operations and effectiveness of the proposed converters for the power grids.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".