A Sensorless Adaptive Maximum Power Point Extraction Method With Voltage Feedback Control for Small Wind Turbines in Off-Grid Applications
Bibliographic record
Abstract
Due to the ever growing global energy demand and pollution levels, clean and renewable alternative energy resources, such as wind, have become indispensable for preserving the planet for future generations. With wind being an unpredictable resource, it is imperative that wind systems extract as much power from the wind as possible while it is available. The conventional maximum power point tracking (MPPT) algorithms that use predetermined mathematical relationships to represent a specific wind system's power/torque characteristics suffer the drawback of deteriorated efficiency over time whereas the perturb and observe algorithms are susceptible to logical errors when subjected to frequent atmospheric variations. In order to solve the aforementioned drawbacks as well as to reduce the cost of the sensing network required to achieve MPPT, this paper proposes a novel sensorless slope-assisted MPPT algorithm that is able to avoid logical errors attributed to wind fluctuations by detecting and identifying atmospheric changes. Atmospheric changes are detected by a state observer by monitoring the generator output power, the ac/dc rectifier output voltage, and the rate of change of the power-voltage ratio without the need for anemometers and any generator speed sensors. The detailed description of the proposed MPPT control logic will be provided in this paper. The functionality of the proposed control method is verified through the simulation results on a 3-kW system, as well as the experimental results on a proof-of-concept 200-W prototype.
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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 | 0.001 | 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".