A single-phase DC-AC dual-active-bridge based resonant converter for grid-connected photovoltaic applications
Bibliographic record
Abstract
In the wake of the global energy crisis, the integration of renewable energy resources, energy storage devices, and electric vehicles into the electric grid has been of great interest towards replacing conventional, fossil-fuel-dependent energy resources. This thesis presents the circuit topology and a control strategy for a 250-W single-phase gridconnected dc-ac converter for photovoltaic (PV) solar applications. The converter is based on the dual active bridge (DAB) kernel employing a series-resonant link and a high-frequency isolation stage. For interfacing the 60-Hz ac grid with the 78-kHz resonant circuit, the converter utilizes a four-quadrant switch array that functions as an ac-ac stage. Therefore, a bipolar low-frequency voltage source, that is the grid voltage, is used to synthesize a symmetrical high-frequency voltage pulse-train for the resonant circuit. Thus, soft switching and the use of a compact ferrite-core transformer have been possible. Then, a fast current-control loop ensures that the converter injects a sinusoidal current in phase with the grid voltage, while a relatively slower feedback loop regulates the converter dc-side voltage, that is, the PV array voltage, at a desired value. To simulate the converter and to design the controllers, the thesis also presents nonlinear large-signal and linearized small-signal state-space averaged models. The performance of the converter is assessed through simulation studies conducted using the aforementioned averaged models, a detailed topological model in the PLECS software environment, and a prototype. Keywords: Photovoltaic, PV, Microinverter, Dual Active Bridge, Phase-shift Modulation, High Frequency Transformer
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".