Modular Power-Balanced Resonant Converters for MVdc Distributed PV Systems
Bibliographic record
Abstract
Research into the use of medium-voltage dc (MVdc) grids is on the rise due to advantages including lower maintenance costs, lower loss, and simpler controllability compared to their ac counterpart. The operating voltage of a typical PV array system is quite low compared to the MVdc level which has led to advances in module-based input independent output series (IIOS) configurations to improve the output voltage level while providing individual maximum power point tracking (MPPT) for each module. Due to varying solar irradiation, atmospheric conditions, and PV manufacturing differences, converter modules will operate at varying power levels leading to modular power mismatch which can result in overvoltage scenarios, damaged components, and ultimately the failure of the system. In this article, a new interconnecting modular full-bridge step-up LLC resonant converter is proposed for IIOS based MVdc PV energy applications. The proposed system’s converter module is capable of regulating power flow between mismatched modules through the use of pulse-width modulation (PWM) control on a new active voltage doubler based voltage balancer (VD). To achieve individual maximum power point tracking (MPPT) in each module, phase-shift control is used on the input full bridge switches of each module. The use of the LLC resonant converter allows for zero-voltage switching operation on all module switches to enhance the system efficiency while stepping up the PV voltage. These configurations allow the modular PV system to achieve simultaneous MPPT, soft-switching operation, and balanced output voltage operation over a wide operating range of PV irradiation levels. The steady-state and dynamic performance of the designed system is validated on a five module 6kW, 12kV-output simulation and a scaled-down 1.6kW, 1.6kV-output two module laboratory prototype.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".