New Optimized Control of Cascaded Multilevel Converters for Grid Tied Photovoltaic Power Generation
Bibliographic record
Abstract
This paper proposes a new optimized control of photovoltaic two stages conversion cascade composed by Three Levels Boost (TLB) and Three Levels Neutral Point Clamped (TLNPC) inverter. In order to extract the maximum power from photovoltaic generator and get a balanced DC bus voltage, the duty cycles of the two TLB switches are determinate from a Fuzzy Logic Controller (FLC) for the first switch and by adding to the first duty cycle an additional duty cycle obtained by integration of the error between the two capacitors voltages of DC bus. Balancing the bus voltages by the TLB using a single regulator avoid us to use a complex balancing algorithm by the redundant vectors of TLNPC inverter. For the control of the inverter, we used a Proportional Integral (PI) regulator optimized by PSO. This command allows us to have on one side a constant DC bus voltage and a current injection in phase with the grid voltage. To have an efficient follow-up of the TLNPC inverter reference voltages, the Space Vector Pulse Width Modulation (SVPWM) is applied. The simulation is carried out in MATLAB/SIMULINK platform. The results obtained from the application of the FLC command associated with PI PSO are better compared to the simulation without optimization in terms of sum of the absolute values of the errors at the inputs of the three PI regulators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".