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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".