Optimization of a Fuzzy-Based MPPT Controller for a PV Water Pumping System Through a PSO-Based Approach
Bibliographic record
Abstract
Water is essential for many agricultural and human needs.The use of fossil fuels in water pumping systems has an effect on the environment.A new energy paradigm is being adopted as part of the sustainable development goals, and carbon-free technologies are being widely used to generate renewable energy.This paper presents a fuzzy-based maximum power point tracking (MPPT) approach for a photovoltaic (PV) water pumping system that employs particle swarm optimization (PSO).Additionally, the fuzzy logic control (FLC) scheme for power converters was used in SIMULINK/MATLAB to design and simulate the MPPT of the PV system.The FLC inputs and output scaling gains were adjusted using the PSO algorithm.In addition, a comparative evaluation of the performance of different MPPT controllers was carried out.It made use of fuzzy logic, a PSO-based fuzzy controller, and the perturb and observe technique.According to the simulation results, the simulated photovoltaic water pumping application has high efficiency levels of a normal fuzzy logic, a PSObased fuzzy controller, and the perturb and observe technique are 95.65%,96.5%, 84.99%, respectively.The results further indicate that the overall efficiency of the PV water pumping system can be significantly increased by using the recommended PSObased fuzzy controller technique.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".