Modeling and Robust Control Design for Distributed Maximum Power Point Tracking in Photovoltaic Systems
Bibliographic record
Abstract
Photovoltaic installations in urban areas operate under uneven lighting conditions. For such a system to achieve its peak efficiency, each solar panel is connected in series through a micro-converter, a dc-dc converter that performs per-panel distributed maximum power point tracking (DMPPT). The objective of this thesis is to design a compensator for the DMPPT micro-converter. A novel, systematic approach to plant modeling is presented for this system, together with a framework for characterizing the plant’s uncertainty. A robust control design procedure based on linear matrix inequalities is then proposed, which ensures robust performance and stability of the time-varying system. The proposed modeling and control design methods are demonstrated for an example rooftop photovoltaic installation. The system and the designed compensator are tested in simulations. Simulation results show satisfactory performance over a range of operating conditions, and the simulated system is shown to track the maximum power point of every panel.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".