A Fast Parabolic-Assumption Algorithm for Global MPPT of Photovoltaic Systems Under Partial Shading Conditions
Bibliographic record
Abstract
Photovoltaic (PV) arrays exhibit multiple local maximum power points (MPPs) in their I-V and P-V characteristic curves when different modules are subjected to different radiations at the same time, known as a partial shading condition (PSC). Hence, tracking the global MPP is crucial to increase the PV system efficiency. Conventional global MPP tracking (GMPPT) algorithms suffer from slow convergence, while some can fail to track the global MPP during PSCs. This article proposes a totally new approach to finding the global MPP during PSC using a single current sensor: the parabolic assumption GMPPT algorithm uses a fixed number of current scans (steps) equal to the number of solar modules connected in series to directly and immediately calculate the global MPP. The proposed algorithm uses simple parabolic equations to calculate the global MPP near-exactly during PSCs. The performance of the proposed algorithm is first evaluated by simulation in MATLAB/Simulink, and then by experimental verification. The results show very fast global MPP tracking and negligible tracking energy loss with an experimental tracking efficiency over 99.6% for the four PSC patterns tested.
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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 | 0.001 |
| 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 | 0.001 |
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".