Design and analysis of robust fuzzy logic maximum power point tracking based isolated photovoltaic energy system
Bibliographic record
Abstract
Summary Photovoltaic (PV) energy is highly promising because of its renewable, green, and environment‐friendly nature. In this article, the design and analysis of an isolated PV system using a push‐pull converter with a fuzzy logic‐based maximum power point tracking (MPPT) algorithm is presented. Furthermore, DC‐DC converters, along with intelligent controllers fed with MPPT algorithms, are used to ensure the maximum extraction of incident energy. The proposed methodology utilizes fuzzy logic MPPT techniques based on an isolated push‐pull boost converter to optimize the power output of PV modules, as well as to achieve isolation and high DC gain for DC/AC inversion. This work also presents a single‐phase inverter with fuzzy logic close loop control analysis with LCL filter design. A Canadian solar panel of 250 W is assumed in this research work, which has an open circuit voltage 59.9 V, short circuit current 5.49 A at 25°C temperature, and 1000 W/m2 irradiance. The voltages are tracked, through the MPPT algorithm. These voltages represent a boost to 340 V DC through push‐pull boost converter and are inverted up to 220 V AC through fuzzy logic voltage source inverter. In addition, a unipolar switching technique is used to remove the total harmonic distortion under linear load. The proposed methodology is simulated in MATLAB/Simulink. The simulation results verify that the proposed methodology can efficiently track the MPPT. Finally, the hardware prototype of the proposed system has been experimentally validated.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".