An intelligent sensor-less supervisory power management control algorithm with a fuzzy logic voltage controller for off-grid wind systems
Bibliographic record
Abstract
Conventional power management schemes for standalone wind systems typically use maximum power point tracking (MPPT) methods to extract maximum power from the wind even when the energy storage has reached its capacity. By doing so, the dummy load suffers from unnecessary stress when the load and energy storage element cannot absorb the excess power. This paper proposes an intelligent sensor-less fuzzy-logic based power management supervisory control scheme that autonomously transitions between an adaptive MPPT control mode and a power limiting control mode to regulate the wind turbine energy extraction. The proposed power management scheme is intended for off-grid applications such as remote telecom base stations. The adaptive MPPT and power regulator (PR) algorithms use a derived pseudo tip speed ratio (pTSR) parameter that correlates the measured power and the output voltage of the rectifier to the system's tip speed ratio (TSR). To enable the algorithm to effectively adapt to the wind system despite parameter shifts due to machine aging, a fuzzy-logic controller is used to drive the system to the voltage references generated by the MPPT and PR algorithms. The operating principles of the proposed control technique will be provided in this paper. Performance results for the proposed algorithm are provided in this paper for a 2kW wind system to highlight the merits of the 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".