Enhanced Performance of PMSG in WECS Using MPPT - Fuzzy Sliding Mode Control
Bibliographic record
Abstract
The wind turbine is a tool used to convert wind energy into electrical energy. This research applies the maximum power point tracking (MPPT) algorithm combined with the fuzzy sliding mode control (FSMC) to produce maximum power in the wind turbine. Addition of fuzzy logic algorithm to sliding mode control to reduce the chattering phenomenon caused by the high switching frequency of the MOSFET in the boost converter. The permanent magnet synchronous generator (PMSG) type of generator with a capacity of 600 watts is used to convert the mechanical energy of the turbine into electrical energy. Tracing the maximum power value of the generator with the MPPT-FSMC algorithm in this study based on the value of the generator output voltage, generator output current, and converter output voltage obtained through simulations on MATLAB / SIMULINK. Comparison of wind turbine performance using MPPT-FSMC and without MPPT is shown as validation of improved wind turbine performance when using intelligent control algorithm.
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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) | 0.000 | 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) | 0.001 | 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".