An Electrolytic Capacitor-Less PV Micro-Inverter Based on CLL Resonant Conversion With a Power Control Scheme Using Resonant Circuit Voltage Control Loops
Bibliographic record
Abstract
A power control scheme with maximum power point tracking based on solely voltage feedback control loops is proposed in this paper for a dc/ac isolated high frequency PV micro-inverter. The presented power circuit topology consists of an integrated continuous conduction mode (CCM) boost converter with an asymmetrical pulse-witch modulation (APWM) controlled CLL step-up resonant converter and a half-bridge grid-side inverter. The presented topology is capable of achieving CCM input current and a wide range of zero voltage switching (ZVS) operation for the front-end stage. The proposed power control scheme, as well as the developed maximum power point tracking (MPPT) control technique utilize the resonant circuit’s resonant capacitor voltage and the APWM input voltage of the resonant circuit only to achieve MPPT and to control the active power of the overall system. By doing so, a large electrolytic input filtering capacitor is not needed. This allows small size film capacitors to be used and improves the system lifetime expectancy. The theoretical analysis and the detailed descriptions of the proposed power control scheme for the presented PV micro-inverter are provided. Simulation and hardware results on a 220 W with 120 Vac, 60 Hz output hardware prototype are presented to demonstrate the performance of the proposed PV inverter system.
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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 | 0.000 | 0.001 |
| 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".