A Comparative Analysis of Perturb and Observe and Fuzzy Logic Control Methods for Maximum Power Point Tracking in Photovoltaic Systems
Bibliographic record
Abstract
Maximum Power Point Tracking (MPPT) techniques play a pivotal role in optimizing the energy harvesting efficiency of photovoltaic (PV) systems. Among the various MPPT algorithms, Perturb and Observe (P&O) and fuzzy logic control have emerged as prominent contenders due to their simplicity and effectiveness. This paper presents a comprehensive comparative analysis of these two methods for MPPT in PV systems. The study employs simulation-based experimentation to evaluate the performance of P&O and fuzzy logic algorithms under varying irradiance level conditions. Efficiency, response time, settling time and stability are among the key performance metrics considered for comparison. Results indicate that while both P&O and fuzzy logic approaches exhibit commendable MPPT performance, they demonstrate distinct advantages and limitations. P&O exhibits rapid convergence to the maximum power point but suffers from oscillations around the optimal operating point. On the other hand, fuzzy logic control offers enhanced stability and robustness against step changes in irradiance levels but may require more computational resources. Simulations of the proposed system are performed in MATLAB Simulink environment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".