Experimental implementation of a novel scheduling algorithm for adaptive and modified P&O MPPT controller using fuzzy logic for WECS
Bibliographic record
Abstract
Summary This article proposes an adaptive and modified Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm, using fuzzy logic step size controller, for a wind energy conversion system (WECS) based on wound rotor synchronous generator. The modified P&O controller uses additional current information of the dc‐link, to decrease power oscillation around MPP, and choose the correct direction of the perturbation, while wind speed increase condition, depending on the sign of dc‐link current change. So, in this contribution, a modified drift‐free P&O algorithm was chosen because it gives better results, but the duty cycle step size in the proposed MPPT controller has been varied adaptively, using a fuzzy logic inference system based on the variation of the rectifier output power and the previous change of the duty cycle to get fast convergence until the MPP is reached in increasing and decreasing wind speed conditions. Performances and robustness of the proposed speed sensorless MPPT controller are evaluated in an experiment implementation, using a WECS emulator and dSpace DS1104 controller board, where experimental results show that the proposed method guarantees fast convergence with better energy quality and high robustness against speed or load change conditions.
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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 | 0.001 |
| 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) | 0.003 | 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".