Adaptive Maximum Power Control Based on Optimum Torque Method for Wind Turbine by Using Fuzzy-Logic Adaption Mechanisms during Partial Load Operation
Bibliographic record
Abstract
This paper aims to increase the effectiveness of the Maximum Power Control (MPC) strategy by using a new Adaptive Maximum Power Control method (AMPC) for maximizing the power delivered by the Wind Turbine System (WTS) during partial load operation whatever the disturbances caused by variations in wind profile. The AMPC is applied to one of the frequently used Maximum Power Point Tracking (MPPT) methods called as Optimum Torque (OT) MPPT algorithm. Furthermore, the proposed AMPC strategy aims to optimize the wind energy captured by the WTS during partial load operation under rating wind speed, using Fuzzy-Logic as Adaption Mechanisms (AMPC- FLC). Additionally, the performances of the proposed improved OT-MPPT method based on AMPC-FLC are compared to the OT-MPPT method based on Conventional Maximum Power Control (CMPC-PI) under the same conditions. The robustness, dynamic performance, and fast approximation of the optimal value are proved with the numerical simulations under MATLAB/Simulink® software.
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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".