Performance Analysis of an Adaptive MPPT Control for a Grid-connected PV Solar System
Bibliographic record
Abstract
This paper presents an adaptive maximum power point tracking (MPPT) control of a grid-connected photovoltaic (PV) energy conversion system utilizing neuro-fuzzy (NF) technique. The particle swarm optimization algorithm is used to train the membership functions while the recursive least squares algorithm is used to update the consequent parameters of the NF based MPPT scheme to cope with changing operating condition of PV solar system. The MPPT algorithm maximizes conversion efficiency by adjusting the duty cycle of the buck-boost converter to change the output voltage of the solar panel and hence, achieving the maximum panel output power for a given set of environmental conditions. The training data for NF scheme is obtained by operating the system using the perturb and observe (PO) MPPT algorithm. The performance of the proposed NF-based MPPT algorithm is validated in both simulation and real-time. The prototype PV system is built and the designed NF-based MPPT algorithm is implemented in laboratory environment using the DSP board DS1104. It is found that the proposed NF-based MPPT scheme achieves a very fast response with minor oscillations while transferring maximum power from solar panel to the grid line as compared to the conventional PO based MPPT scheme.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".